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MoWIE for Network Aware Application
draft-huang-alto-mowie-for-network-aware-app-05

Document Type Active Internet-Draft (individual)
Authors Yuhang Jia , Yunfei Zhang , Richard Yang , Gang Li , Yixue Lei , Yunbo Han , Sabine Randriamasy
Last updated 2022-10-20
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draft-huang-alto-mowie-for-network-aware-app-05
ALTO                                                              Y. Jia
Internet-Draft                                                  Y. Zhang
Intended status: Standards Track                                 Tencent
Expires: 26 April 2023                                        Y. R. Yang
                                                         Yale University
                                                                   G. Li
                                                                    CMRI
                                                                  Y. Lei
                                                                  Y. Han
                                                                 Tencent
                                                     Sabine. Randriamasy
                                                                   Nokia
                                                         23 October 2022

                  MoWIE for Network Aware Application
            draft-huang-alto-mowie-for-network-aware-app-05

Abstract

   With the deployment of 5G networks, cloud-based interactive services
   such as cloud gaming have gained substantial attention.  To ensure
   users' quality of experience (QoE), a cloud interactive service may
   require not only high bandwidth (e.g., high-resolution media
   transmission) but also low delay (e.g., low latency and low lagging).
   However, the quality perceived by a user with mobile and wireless
   device may vary, as a function of many factors, and unhandled changes
   can substantially compromise the user's QoE.  In this document, we
   investigate network-aware applications (NAA), which realize cloud-
   based interactive services with improved QoE, by efficient
   utilization of a solution named Mobile and Wireless Information
   Exposure (MoWIE).  In particular, this document demonstrates, through
   realistic evaluations, that mobile network information such as MCS
   (Modulation and Coding Scheme) can effectively expose the dynamicity
   of the underlying network and can be made available to applications
   through MoWIE; using such an information, the applications can then
   adapt key control knobs such as media codec schemes, encapsulation,
   and application layer processing to minimize QoE distortion.  Based
   on the evaluations, we discuss how the MoWIE features can define
   extensions of the ALTO protocol, to expose more lower-layer and finer
   grain network dynamics.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

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Table of Contents

   1.  Introduction of Network-aware Applications  . . . . . . . . .   3
   2.  Use Cases of Network-Aware Application (NAA)  . . . . . . . .   5
     2.1.  Cloud Gaming  . . . . . . . . . . . . . . . . . . . . . .   5
     2.2.  Low Delay Live Show . . . . . . . . . . . . . . . . . . .   6
     2.3.  Cloud VR  . . . . . . . . . . . . . . . . . . . . . . . .   6
     2.4.  Performance Requirements of these Use Cases . . . . . . .   6
   3.  Current (Indirect) Technologies on NAA  . . . . . . . . . . .   7
     3.1.  Video Compression Based on ROI (Region of Interest) . . .   7
     3.2.  AI-based Adaptive Bitrate . . . . . . . . . . . . . . . .   8
   4.  Preliminary QoE Improvement Based on MoWIE  . . . . . . . . .   9
     4.1.  MoWIE Architecture and Network Information Exposure . . .   9
     4.2.  RAN-assisted TCP optimization based on MoWIE  . . . . . .  12
     4.3.  NAA QoE Test based on MoWIE . . . . . . . . . . . . . . .  12
     4.4.  ROI Detection with Network Information  . . . . . . . . .  13
     4.5.  Adaptive Bitrate with Network Capability Exposure . . . .  15
     4.6.  Analysis of the Experiments . . . . . . . . . . . . . . .  17
   5.  Standardization Considerations of MoWIE as an Extension to
           ALTO  . . . . . . . . . . . . . . . . . . . . . . . . . .  18
   6.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  24
   7.  Security Considerations . . . . . . . . . . . . . . . . . . .  24

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   8.  Acknowledgments . . . . . . . . . . . . . . . . . . . . . . .  24
   9.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  24
     9.1.  Normative References  . . . . . . . . . . . . . . . . . .  24
     9.2.  Informative References  . . . . . . . . . . . . . . . . .  24
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  27

1.  Introduction of Network-aware Applications

   With the deployment of 5G networks
   [draft-ietf-dmm-5g-uplane-analysis], more applications are available
   as remote cloud-based applications (e.g., cloud AR/VR/MR).  Many
   conventional interactive, daily business applications are also
   becoming widely used as cloud applications, with the help of mobile
   networks and cloud, e.g., cloud video conference.  Especially, during
   the coronavirus pandemic in 2020, many people had to stay at home and
   work/study remotely, and the usage of cloud applications, including
   cloud-based online courses, cloud-based conferencing, and cloud
   gaming, have surged significantly.

   To optimize QoE for end users using mobile networks (a.k.a., cellular
   networks), many cloud applications utilize information about the
   mobile network status, e.g., delay, bandwidth, and jitter, to
   dynamically balance the generated media traffic and the rendering/
   mixing in the cloud.  Currently, such an application assumes the
   network as a link and continuously uses client or server measurements
   to detect network characteristics, and then adaptively changes its
   network-related configuration parameters which may impact QoS
   handling as well as logical functions to support the upper layer
   application.  However, when only application information is utilized,
   the QoE that an application can achive can be limited in some cases.
   First, information from the application side (i.e., the 3rd party
   application server) may have relatively long delay.  When a user
   enters a location with a bad network connectivity, such as an
   elevator or an underground garage, the application will not receive
   such an information immediately.  As a result, the buffer of video
   application may have a high chance to run out.  Then the screen will
   freeze and users' QoE will be harmed.  Besides, the application does
   not have information about other users in the cell.  Thus, it cannot
   know how many resources it can get and when it will change.  If other
   users enter the cell and compete on the usage of the resource, the
   application layer may misjudge the resource and request a high
   bitrate.  Then the delay will increase and QoE will drop.  Some
   information from the network layer like physical resource block (PRB)
   information and utilization rate can help applications to describe
   how many resources the user will get and how many users are competing
   with it.  Such an information is helpful to predict the network and
   streaming videos.  However, the application cannot get those kinds of
   information yet.  Because 5G network deployed by the mobile network

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   operation which is normally in a different domain compared with
   internet-based applications with 3rd party, the application server
   may not be able to directly get the network internal status without
   network exposure.  Therefore, there have been continuous efforts in
   3GPP enable network exposure to serve the 3rd party applications in a
   better way in terms of network resource utilization and user's
   experiences.

   The 3GPP mobile networks are always adhering to standard solutions to
   get network dynamic indicators that can be used by applications.  In
   3GPP, many IP-based QoS mechanisms are used.  For example, ECN
   [RFC3168] has been supported by the 4G radio station (eNB) to provide
   CE(Congestion Encountered) information to the IMS application to
   perform Adaptive Bitrate (ABR) [TS26.114].  The application can
   downgrade the bit rate after receiving the CE indication, but does
   not know the exact bit rate to be selected.  DSCP [RFC2474] is used
   to identify the QoS class and paging strategy [TS23.501], but
   typically the application cannot dynamically change the DSCP to
   improve the bit rate based on the network status by mapping DSCP with
   4G QCI or 5G 5QI can be a good approach to realize end-to-end QoS.
   DASH [MPEGDASH] is an MPEG standard widely used for the applications
   to detect the throughput of the network based on the current
   throughput and buffering states and adaptively select the next
   segment of video streaming with a suitable bitrate in order to avoid
   re-buffering.  SAND-DASH [TS26.247] defines the mechanism that the
   network/server can provide available throughput to the applications;
   in such case, the better bitrate can be selected by DASH application.

   There are some early works on network status exposure to TCP server,
   which may also indirectly impact application layer optimization, but
   did not consider 5G networks [Sprecher_mobile], [flinck_mobile] In 5G
   cellular networks, network capability exposure has been specified to
   allow the 5G system (5GS) to expose user device location and network
   status towards the 3rd party application servers modeled as AF
   (Application Function) [TS23.501].  In such a case, the AF can
   request the 5GS to establish a dedicated QoS Flow to transport an IP
   flow with the AF-provided QoS requirements.  Via certain
   measurements, network internal status including congestion can be
   exposed and optimization can be carried out for the cellular network
   [PBECC].

   5G also can provide QNC (QoS Notification Control) to an AF if the
   GBR(Guaranteed Bitrate) of the established GBR QoS Flow cannot be
   fulfilled, and the AF can change the bitrate after receiving the QNC
   notification.  But the AF still does not know which bitrate to be
   selected.  So 5G enhances the QNC by providing a list of AQPs
   (alternative QoS profiles).  With AQP, a 5G network provides a subset
   of supported AQPs with the QNC, and then the AF selects a bit rate

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   from 5G network supported AQPs.  In such a case, the GBR can be
   fulfilled again if the radio state of the UE is changed.  QoS
   prediction is realized by network function inside 5GC to collect and
   analyze the status and parameters from the 5G network entities, and
   deliver the analytics results to an entity such as application
   server.

   However, both network capability exposure and QoS prediction
   solutions are designed for 5G access and core networks, which cannot
   cover the whole end-to-end network.  How to enable the application
   which locates the internal DN (data network) to be aware of the lower
   layer networks within 5G network are an important area for both
   industrial and academic researchers, that is addressed by MoWIE.

   MoWIE is a solution that aims to realize real-time provisioning of
   cellular radio network information by networks to applications, thus
   helping service providers to achieve a better policy control and to
   improve user experience.  The benefits of the MoWIE concept/solution
   have been experimented on several use cases, detailed in Section 4.1.

2.  Use Cases of Network-Aware Application (NAA)

   There are three typical NAAs, cloud gaming, low-delay live shows, and
   cloud VR, whose QoE can be largely enhanced with the help of MoWIE.

2.1.  Cloud Gaming

   As mentioned above, cloud gaming is widely used, and this kind of
   games requires low latency and highly reliable transmission of motion
   tracking data from the user to the gaming server in the cloud, as
   well as low latency and high data rate transmission of processed
   visual content from gaming server cloud to the user devices.  Cloud
   gaming is regarded as one major killer application as well as traffic
   contributor to wireless and cellular networks including 5G.  The
   major advantages of cloud gaming are easy & quick starting (no/less
   need to download and install a high-volume software in the user
   device), less cost and process load in the user device and it is also
   regarded as an anti-cheating measure.  Thus, cloud gaming has become
   a competitive replacement for console gaming using cheaper PC or
   laptop.  To support high quality cloud gaming services, the
   application needs to get the information from the network layer,
   e.g., the data rate value or range which lower layer can provide in
   order to perform rendering and encoding, during which the application
   in the cloud can adopt different parameters to adjust the size of
   produced visual content within a time period.

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2.2.  Low Delay Live Show

   In 2019, over 500 million active users were using online personal
   live show services in China and there are 4 million simultaneous
   online audience watching a celebrity's show.  Low delay live show
   requires close interaction between the application and the network.

   Compared with conventional broadcast services, this service is
   interactive, which means that audience can be involved and they are
   able to provide feedback to the anchor.  For example, a gaming show
   broadcasts the gaming playing to all audiences, and it also requires
   playing game interaction between the anchor and audiences.  A delay
   lower than 100 ms is desired.  If the delay is too large, there will
   be undesirable degradation on user experiences especially in a large-
   scale show.  To lower the latency and provide size-adjustable show
   content, the application also requires real-time lower layer
   information.

2.3.  Cloud VR

   Cloud VR data volume is large which is related to different parameter
   settings like DoF (Degree of Freedom), resolution and adopted
   rendering and compression algorithm.  The rendering can be performed
   at the cloud/network side or a mix of the cloud and the user device
   side.  Because the latency in cloud VR is even as low as 20 ms, the
   application may need to interact with network to get the information
   about the segmentation or transport block information, and these
   lower layers information may be dependent on different layer 2 and
   layer 3 wireless protocol designs.

2.4.  Performance Requirements of these Use Cases

   There are different bandwidth, latency and lagging requirements for
   the above services which are characterized as parameter range.  The
   reason of using a range is because such requirements are related to a
   group of parameter settings including resolution, frame rate (FPS,
   frame per second) and the compression mechanism.  We consider
   1080p~4K as the resolution range, 60-120 FPS (Frames per second) as
   the frame rate and H.265 as an example compression algorithm.  The
   end-to-end latency requirement is not only related to FPS but also
   the property of the service, i.e., for weak interactive and strong
   interactive services.

   With the typical parameters setting, cloud gaming generally needs a
   bandwidth of 20~60 Mbps; we also consider that significant lagging
   happens when the latency is larger than 200 ms, depending on the
   types of games (e.g. 40 ms for First Person Shoot games, 80 ms for
   Action games, and 200 ms for Puzzle games).  In order to avoid bad

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   user experiences, lagging is better when it is lower, and can be as
   low as zero (in an optimal QoE).  For low latency live shows, 20~50
   Mbps bandwidth may be needed and the end-to-end latency requirement
   is less than 100 ms.  Cloud VR service generally requires 100~500
   Mbps bandwidth and 20~50 ms end-to-end latency.  It is noted that
   these values are dependent on the parameter settings and they are
   provided to illustrate the order of magnitude of these parameters for
   the aforementioned use cases.  These value range may be updated
   according to specific scenarios and requirements.

3.  Current (Indirect) Technologies on NAA

   The applications have tried to increase QoE with the help of network
   information captured from the application layer to guess the network
   dynamics, such as bitrate, buffer status, and packet loss rate.

   For example, adaptive bitrate (ABR) and buffer control methods to
   reduce delay, and application layer forward error scheme (AL-FEC) to
   avoid packet losing are proposed.  This document focuses on two novel
   approaches, which have achieved good performance in practice.  One is
   video encoding based on ROI, the other is reinforcement learning
   based adaptive bitrate.

3.1.  Video Compression Based on ROI (Region of Interest)

   A foveated mechanism [Saccadic] in the Human Visual System indicates
   that only small fovea region captures most visual attention at high
   resolution, while other peripheral regions receive little attention
   at low resolution.  And we call those regions which attract users
   most, the regions of interest (ROI)[Fahad].

   To predict human attention or ROI, saliency detection has been widely
   studied in recent years, with applications in object recognition,
   object segmentation, action recognition, image caption, image/video
   compression, etc.

   Since there exists the region of interest in a video, the cloud
   server can give the ROI region higher rate while making other regions
   a lower rate.  As a result, the whole rate of the video is reduced
   while the watching experience will not be harmed.

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   This method means to detect the ROI and re-allocate the coding scheme
   for interested and non-interested regions in order to save the
   bandwidth without sacrificing user's QoE.  In recent years, the ever-
   increasing video size has become a big problem to applications.  The
   data rate of a cloud gaming video in 1080P can reach 25 Mbps, which
   brings huge burden to the network, even for 5G network.  Those ROI-
   based video compression methods are mainly applied to high
   concurrency networks to relive the burden of networks and then keep
   QoE in an acceptable range.

   However, current methods utilize application information like
   application rate and application buffer size as the indicators to
   roughly adjust the algorithm in interactive video services.  That
   information is hard to reflect the real-time network status
   precisely.  Therefore, it is hard to balance the QoE and bandwidth
   saving in real-time scenario.  More direct information is helpful for
   those ROI methods to improve the performance.

3.2.  AI-based Adaptive Bitrate

   This method intends to reduce lagging and ensure acceptable picture
   quality.

   Applications such as video live streaming and cloud gaming employ
   adaptive bitrate (ABR) algorithms to optimize user QoE [MPC][CS2P].

   Despite the abundance of recently proposed schemes, state-of-the-art
   AI based ABR algorithms suffer from a key limitation.  They use fixed
   control rules based on simplified or inaccurate models of the
   deployment environment.  As a result, existing schemes inevitably
   fail to achieve optimal performance across a broad set of network
   conditions and QoE objectives.

   A reinforcement learning based ABR algorithm named Pensieve was
   proposed [Hongzi] recently.  Unlike traditional ABR algorithms that
   use fixed heuristics or inaccurate system models, Pensieve's ABR
   algorithms are generated using observations of the resulting
   performance of past decisions across a large number of video
   streaming experiments.  This allows Pensieve to optimize its policy
   for different network characteristics and QoE metrics directly from
   experience.  Over a broad set of network conditions and QoE metrics,
   it has been proven that Pensieve outperformed existing ABR algorithms
   by 12%~25%.

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   For this method and those methods built upon this, it has been proven
   that all information, including rate, download time, buffer size or
   network level information which can reflect the performance, are
   useful to reinforcement learning.  Since those data can reflect the
   network dynamics, they have been used to help the applications to
   know how to change the rate and improve users' QoE.

   However, all these data are obtained from the client side or the
   server side.  In reality, it is not easy to obtain such data in an
   effective and efficient way.  The lack of standardized approach to
   acquire these data makes it difficult to make this usable for
   different applications for large scale deployment.  Meanwhile, since
   these data reflect the real-time network status, they may change
   rapidly and randomly, and hence can be hard to use a theoretical
   model to characterize.

   To summarize, current practices can make some improvements by
   indirectly measuring network status and react in the application.

   However, the network status data are not rich, direct, real-time;
   they also lack predictability, especially when in the mobile and
   wireless network scenarios, which results in long reaction delay or
   high QoE fluctuations.

4.  Preliminary QoE Improvement Based on MoWIE

4.1.  MoWIE Architecture and Network Information Exposure

   The fundamental idea of MoWIE is to achieve on demand and periodic
   network information from network to applications, helping network
   service providers to realize a better policy control and to improve
   users' experience.

   A possible MoWIE architecture includes three core components: the
   Client Application, the Mobile Network and the Application Server.

   The raw data are collected firstly from the radio network and core
   network; further processing on these collected data and the exposure
   of Network information are provided to the application Server.

   An application server can send network information request about UE/
   Cell level information and obtain the NIS response on network
   information from the mobile network.  After user data pre-processing,
   the application server will make best use of the network information
   to perform analytics and directly enhance the application functions
   e.g. bit rate, latency, and jitter.

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   Typically, the network information provided by MoWIE includes two
   types of information as below:

   Cell level Information:

   *  The number of Downlink PRBs (Physical Resource Blocks) occupied
      during sampling period;

   *  the cell load;

   *  the downklink (DL) MAC data rate per cell;

   *  the channel status (e.g.  RSRP (Reference Signal Received Power)
      and CQI (Channel Quality Indicator));

   *  the DL data rate;

   *  the PDCP (Packet Data Convergence Protocol) buffer status;

   UE level information (without privacy information):

   *  The Downklink Signal to Inference plus Noise Ratio (SINR);

   *  MCS: The index of Modulation and Coding Scheme (MCS);

   *  The number of packets occupied in PDCP buffer;

   *  The number of downlink PDCP Service Data Unit (SDU) packets;

   *  The number of lost PDCP SDU packets;

   *  The per UE downlink MAC data rate;

   *  the per-UE channel status (e.g.  RSRP (Reference Signal Received
      Power) and CQI (Channel Quality Indicator));

   *  the per-UE DL data rate;

   *  the per-UE PDCP (Packet Data Convergence Protocol) buffer status;

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   The network information listed here can also be found in 3GPP (PRB
   [TS38.211], cell load [TS38.300] PDCP for 5G [TS38.323] RSRP, RSRQ,
   RSSI [TS38.331], MCS, CQI [TS38.214], The number of packets occupied
   in PDCP buffer, the number of Downlink PDCP SDU packets, the number
   of PDCP SDU packets lost, the per-UE PDCP buffer status [TS38.323]),
   to demonstrate the potential benefits of MoWIE for network-
   application integration over cellular network.  Figure 3-1 and
   Figure 3-2 list the data types correponding to the cell-level
   information and UE-level information, respectively.

               +----------------------+--------------------------+
               |Cell-level Information|     Data type/Range      |
               +----------------------+--------------------------+
               |         PRB          |          Uint16          |
               +----------------------+--------------------------+
               |         CQI          |          Uint8           |
               +----------------------+--------------------------+
               |        RSRP          |          Uint8           |
               +----------------------+--------------------------+
               |        RSRQ          |          Uint8           |
               +----------------------+--------------------------+
               |     Cell load        |          [0,1]           |
               +----------------------+--------------------------+
                           Figure 4-1: Cell level data type

               +------------------------------------+---------------+
               |         UE-level Information       |Data type/Range|
               +------------------------------------+---------------+
               |            Downlink SINR           |     Uint16    |
               +------------------------------------+---------------+
               |                MCS                 |     Uint8     |
               +------------------------------------+---------------+
               |      Downlink PDCP SDU packets     |     Uint8     |
               +------------------------------------+---------------+
               |    PDCP SDU packets lost           |     Uint8     |
               +------------------------------------+---------------+
               |    Packets occupied in PDCP buffer |     [0,1]     |
               +------------------------------------+---------------+
               |                CQI                 |     Uint8     |
               +------------------------------------+---------------+
               |                RSRP                |     Uint8     |
               +------------------------------------+---------------+
               |                RSRQ                |     Uint8     |
               +------------------------------------+---------------+
                           Figure 4-2: UE level data type

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4.2.  RAN-assisted TCP optimization based on MoWIE

   The RAN information is used to assist TCP sending window adjustment
   rather than traditional transport layer measurement and
   acknowledgement.  The RAN proactively predicts available radio
   bandwidth and the buffer status per UE in a time granularity of RTT
   level (e.g. 100 ms) and then piggybacks such information in TCP ACK.

   We have conducted trial in real mobile networks.  It is observed that
   for the UE with good SINR, the throughput is significantly improved
   by nearly 100%, and the UE with medium SINR can achieve approximately
   50% gain.

4.3.  NAA QoE Test based on MoWIE

   Different from traditional video streaming, cloud gaming has no
   buffer to accommodate and re-arrange the received data.  It must
   display the stream once the stream is received.  Any late stream is
   of no use for the player.  Cloud gaming performs not well in the
   existing public 4G network according to our actual measurements.  The
   end to end delay is often greater than 100 ms for a gaming client in
   Shenzhen to a gaming server in Shanghai, coupled with the codec
   delay.  Here the delay is defined as the total delay from the user's
   operation instruction to show the response picture on user's screen.

   Once the network fluctuates, users will experience a longer delay.

   The poor user experience is not only because of the relative low
   network throughput, but also because that the server cannot adapt the
   application logical policies (e.g., codec scheme and data bitrate).

   The popularity of 4K and even higher resolution and increasing FPS
   for cloud gaming and AR/VR services require both high bandwidth and
   low latency in wireless and cellular networks.  The increasing
   resolution would incur a higher encoding and decoding delay.
   However, users' tolerance to delay will not increase with the
   resolution, which means the application needs to adapt to the network
   dynamics in a more efficient way.  The higher resolution, the larger
   range of the rate adaptation can be used.

   In this section, we make experiments based on the methods described
   in section 3 to improve the QoE of cloud gaming.  The performance
   between network-aware and native non-network-aware mechanisms are
   compared.

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4.4.  ROI Detection with Network Information

   The first experiment is based on the ROI detection.  We will
   investigate the impact of network perception.

   Saliency detection method has successfully reduced the size of videos
   and improve the QoE of users in video downloading [Saliency].

   However, it is not effective when applied to real-time interactive
   streaming such as cloud gaming.

   As we know, more accurate saliency region detection algorithm needs
   more time to obtain the result.  However, when the users are
   suffering a bad performance network in cloud gaming, this precise
   detection may incur more delay to the system.  As a result, it will
   harm the final QoE.

   If the application can learn the network well in a real-time manner,
   it can choose the algorithm based on how much delay the system can
   tolerate.  If the network condition is good enough, it can adopt an
   algorithm which has deeper learning network and the added delay will
   not be perceived by the end users.  Thus, it can save huge bandwidth
   without harming the QoE.  On the other side, in a network with bad
   condition, the server can use the fastest method to avoid extra
   delay.

   We make the experiments to show how the network information will
   influence the total QoE and bandwidth saving in ROI detection.

   The following 4 methods are compared:

   1) The original video, without using ROI method.  This acts as a
   baseline.

   2) Quick saliency detection and encoding method, which is not
   accuracy in some cases.  It only brings 10ms delay.

   3) A relative accuracy saliency detection method.  In general, if an
   algorithm is more precise, it will take more time to get the results.

   And the complexity of the picture will also influence the detection
   time and accuracy.  Based on our test video, we adopt the method
   which brings delay about 40~70ms.

   4) The application server in the cloud has the current bandwidth
   information which derived from the wireless LAN NIC.  Here it is a
   simulation that all the collected bandwidth traces are already known
   by the server.  Thus, it can use the bandwidth traces to compute

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   transmission delay.  Then the server can change the saliency
   detection algorithm based on this information and then encode the
   video.

   Although the result of future bandwidth prediction is not always
   accurate in real environment, the assumption here will not influence
   the final results much.  Since in cloud gaming the server encodes the
   stream based on ROI information frame by frame instead of in a grain
   of chunks, the future bandwidth prediction window size doesn't have
   to be long.  Therefore, even the server can only get the bandwidth or
   delay prediction for a short time window, the server can still use
   this method with network information.

   Test environment:

   A 720P game video segment with a rate of 6.8Mbps.  This is not a very
   high bandwidth requirement example in cloud gaming.  We just show how
   it will benefit from MoWIE.  High bandwidth requirement case will
   benefit more if the bandwidth fluctuates much.

   The three different networks are all wireless networks and the
   available bandwidth is varied frequently, where Network 1: The
   overall network condition is not very good, the average network
   bandwidth is 7.1Mbps, but it continues to fluctuate, and the minimum
   is only 3.9Mbps.

   Network 2: The overall network condition is good, with an average
   network bandwidth of 12Mbps and a minimum of 6.4Mbps.

   Network 3: The network fluctuates dramatically, with an average
   network bandwidth of 8.4Mbps and a minimum network bandwidth of
   3.7Mbps

   Test content:

   The four methods are conducted on the original video under each three
   networks.  After re-encoding based on the saliency detection, we
   calculate the new QoE and the saved bandwidth.  The results are shown
   in the Figure 4-1 (The QoE value is the MOS as standardized in the
   ITU):

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       +---+-----------------+-----------------+-----------------+
       |   |    Network 1    |    Network 2    |    Network 3    |
       +---+---+-------------+---+-------------+---+-------------+
       |   |QoE|  BW Saving  |QoE|  BW Saving  |QoE|  BW Saving  |
       +---+---+-------------+---+-------------+---+-------------+
       | 1 |3.8|      0      |4.8|      0      |4.3|     0       |
       +---+---+-------------+---+-------------+---+-------------+
       | 2 |3.8|     5%      |4.8|     9%      |4.3|    7%       |
       +---+---+-------------+---+-------------+---+-------------+
       | 3 |2.2|    2.1%     |4.6|     38%     |3.1|    34%      |
       +---+---+-------------+---+-------------+---+-------------+
       | 4 |3.6|     9%      |4.7|     33%     |4.3|    25%      |
       +---+---+-------------+---+-------------+---+-------------+
                  Figure 4-3: QoE and Bandwidth Saving

   Conclusion:

   It can be seen that the methods such as method 2 and method 3 that do
   not rely on the network information directly, have certain
   limitations.

   Though the method 2 is simple and time-consuming, it can only detect
   a small part of region of interest accurately.  Thus, even if the
   network condition is very good, it can only save a small amount of
   bandwidth, and sometimes there are some incorrect ROI detection.  The
   QoE will be reduced without hitting the ROI region.

   For Method 3, the algorithm is complicated, and it can correctly
   detect the user's area of interest, so that it can re-allocate
   encoding scheme and save a lot of bandwidth.  However, its algorithm
   will introduce higher delay.  When the user network condition is
   poor, the extra delay will cause even worst user's QoE.  Although the
   bandwidth is saved, it affects the user experience seriously.

   Method 4 is based on the application's awareness of the network.  If
   the application can know certain network information, it can balance
   the complexity of the algorithm (introducing delay) and the accuracy
   of the algorithm (saving bandwidth) according to the actual network
   conditions.  As can be seen from the experiment, method 4 can ensure
   the user's QoE and save the bandwidth greatly at the same time.

4.5.  Adaptive Bitrate with Network Capability Exposure

   This experiment is AI-based rate adaption by utilizing the network
   information provided by the cellular base station (gNB) in cellular
   network.

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   Tencent has launched real network testing of NAA-enabled cloud gaming
   in China Mobile LTE network, with the enhancement in gNB supporting
   base station information exposure.

   To enable the NAA mechanism, some cellular network information from
   gNBs are collected in an adaptive interval based on the change rate
   of network status.  There information is categorized in two levels,
   i.e., cell level and UE level.  Cell level information are common for
   all the UEs under a serving LTE cell and UE level information is
   specific for different UEs. 3GPP LTE specifications have specified
   how the PDCP, RLC (Radio Link Control), MAC (Medium Access Control)
   and PHY (Physical) protocols operate and this information are very
   essential statistics from these protocol layers.

   It is noted that in NAA mechanism, as the network information is from
   gNB, and the gNB has the real-time information of radio link quality
   statistics and layer 1 and layer 2 operation information, NAA
   mechanism can expose rich information to upper layer, e.g., it is
   capable to differentiate packet loss and congestion [MengZ], which is
   very helpful to the applications in practice.

   In order to compare the cases with and without NAA, the cloud gaming
   test environment is setup with 1080p resolution and around 20Mbps
   bitrate.

   Test scenarios 1~5 are as follows.

   Test scenarios 1: Weak network.  This scenario is the case where
   radio link quality is low, e.g., in cell edge area and the bandwidth
   is not able to serve cloud gaming.

   Test scenario 2: User competition scenario.  This scenario is defined
   as the case when user amount is large thus the cellular network
   bandwidth cannot serve all the cloud gaming users.

   Test scenario 3-5: Other scenarios with random user movement trace
   and user distribution.

   Test method: To simplify to comparison, we just use the MCS (MCS
   index) information derived from the gNB [TS38.214].  The information
   is provided directly to the application, and the application then
   adjusts the bit rate according to this information.  Here, MCS index
   shows the modulation (e.g.  QPSK, 16QAM,...) and the coding rate used
   during physical layer transmission, which is relevant to the real
   data rate per UE.  The benchmark method is adopting a constant bit
   rate without any information to help it predicting the network
   condition.  We compare these scenarios and observe the reduction of
   delay when those gNB data are utilized.

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   For different scenarios, the lagging rate is defined as the
   performance indicator.  In our experiments, we assume lagging happens
   when transmission delay is greater than 200ms and lagging rate is
   defined as the ratio between the number of frames greater than 200ms
   and the total number of frames.

               +-------------+--------------------------+
               |Test Scenario| Reduction of Lagging Rate|
               +-------------+--------------------------+
               |     1       |           46%            |
               +-------------+--------------------------+
               |     2       |           21%            |
               +-------------+--------------------------+
               |     3       |           37%            |
               +-------------+--------------------------+
               |     4       |           56%            |
               +-------------+--------------------------+
               |     5       |           32%            |
               +-------------+--------------------------+
                 Figure 4-4: Reduction of Lagging Rate

   It can be clearly seen that with the MCS information, the application
   can adjust the bit rate to decrease the lagging rate and then
   significantly improve the user QoE.  In weak network scenario, 46%
   lagging can be avoided by NAA.

4.6.  Analysis of the Experiments

   The above-mentioned technologies demonstrate the performance gain of
   NAA with MoWIE.

   Although application information can also help to predict the network
   and have already been used in adaptive bit rate methods, the
   application information is not as sensitive as gNB information at the
   very beginning in a lot of cases.  For example, when more users enter
   the cell, the PRB information will first reflect that each user may
   get less bandwidth.  However, the application information needs to
   react after there is a trend that the bitrate is decreasing.  That is
   to say, the lower layer network information is more directly.

   Without MoWIE, the application cannot get the lower layer network
   information directly and then try to detect "blindly" to adapt to the
   dynamics of the lower layer network, which cannot meet the
   requirements of cloud interactive applications like cloud gaming, low
   delay live show and Cloud VR.

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   It is noted that the more real-time network resource status the
   application can learn, the better it can predict how much network
   resource it can use within a prediction time window.  However, there
   is tradeoff between network information collection frequency and its
   load and feasibility to the network devices.  In principle, the total
   network resource consumed for such network status reporting is also
   designed in light-weight manner, e.g., by properly controlling the
   interval of report and also the number of bits needed to convey the
   reported information elements.  In our experiments, the network
   status information can be obtained in an adaptive interval based on
   the change rate of network status, in order to provide good
   prediction with less load introduced in the network.  In fact, not
   all scenarios need a very frequent information collection.  If some
   information only changes in a very small range and won't influence
   the final decision, it is unnecessary to report such information all
   the time.  However, if its value varies over the preset threshold, it
   will inform the application immediately.

   The distribution and impact of the exposed data to the performance
   gain for different algorithm needs to be further studied.  This draft
   is to give a guidance to figure out what kind of data needs to be
   exposed during initial deployment of these mechanisms.

   In our current cloud gaming, the application information can help to
   reduce about 50% the lagging rate.  The left 50% improvement room can
   be achieved by network information exposure with MoWIE.  Actually,
   the effect of the two-layer information can be accumulated.  However,
   due to current deployment limitation, we cannot collect the
   application information with the gNB information at the same time.
   Thus, in this version of the draft we compare the performance with
   and without MoWIE.  We don't compare between application information
   assisted mode and network information assisted mode in this draft.
   This is our on- going work.  Since both application and gNB
   information can reflect the network variation, we will compare the
   performance among application information assisted mode, network
   information assisted mode and the mode of utilizing both layer
   information.

5.  Standardization Considerations of MoWIE as an Extension to ALTO

   In 3GPP, network information exposure based on control plane
   mechanism is introduced in 4G and 5G systems.  In 3GPP Release 17,
   there is a work item named 5G_AIS (Advanced Interactive Services)
   which focuses on QoS enhancements for interactive services including
   cloud gaming, XR, remote driving and real-time digital twin.  There
   is also continuous work in Release 18 XRM (XR and media services),
   which focuses on strengthening the interaction and collaboration
   between applications and networks by improving the ability of the

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   network information exposure especially for XR and multimedia
   services.  MoWIE is closedly related to this study as it can be a
   tool to support exposure of 5G network status information.  MoWIE can
   also support QoS prediction technologies which is being applied to
   5G-enabled automated driving which is being developed in 5GAA
   [PQoS_white_paper], this solution can use network information
   exposure to predict the future network changes to the application
   layer in order to be prepared for such changes and make adaptations
   in application layer to improve the QoE of users.  There have been
   close collaboration between 5GAA and 3GPP to provide E2E solutions on
   this area.

   Among these above mentioned work items, one important way to support
   QoS (MoWIE and PQoS) enhancements is to expose the network status
   information to the application layer and the application layer can
   take measures to adapt according to the network status information.
   The network information can include the radio network statistics as
   has been elaborated in Section 4.1 and also the parameters specified
   in [ALTO_METRICS].  In Section 4.1, the parameters which MoWIE
   proposes to expose can provide real-time status and rich information
   about the wireless link which can be utilized by AI-ML (Machine
   Learning) algorithms to predict the available network resources in
   the subsequent transmission opportunities, which can help the
   application layer to adjust its traffic pattern or codec profile to
   optimize the user's experience.  By mapping these parameters with the
   ALTO metrics which has been proposed or defining potential new ALTO
   metrics, it is possible to extent current ALTO protocols to provide
   better support for real-time immersive services.  In ETSI MEC, RNIS
   [ETSI_MEC] has proposed to expose physical layer, Layer 2 and higher
   layer parameters including 4G and 5G.  There are some common
   parameters like RSRP, RSRQ and RSSI which MoWIE also proposes;
   however, RNIS is based on the MEC architecture, and MoWIE is not
   restricted to MEC case.

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   It should be noticed that the previous mechanisms may also work on
   IEEE 802.11 standards (e.g., EHT), helping SP to have a better
   understanding for the network environment between AP and STAs.  Based
   on the fact that 802.11 devices are working on unlicensed spectrums,
   and easily influenced by adjacent unlicensed devices, duty cycles and
   related CQI information (e.g., MCS, and bandwidth) are considered
   very important network information here.  Standardization
   Considerations of MoWIE as an Extension to ALTO MoWIE can be a
   realistic, important extension to ALTO to serve the aforementioned
   use cases, in the setting of the newer generation (5G) of cellular
   network, which is a completely open IP based network where routers/
   UPF with IP connectivity will be deployed much closer to the users.
   One may consider not only the aforementioned cloud-based multimedia
   applications, but also other latency sensitive applications such as
   connected vehicles and automotive driving.

   Extending ALTO with MoWIE, therefore, may allow ALTO to expose lower
   layer network information to ensure higher application QoE for a wide
   spectrum of applications.

   One possible approach to standardizing the distribution of the
   network information used in the evaluations is to send such
   information as piggyback information in the data path.  One issue
   with data path method is that MoWIE intends to convey more complex
   and rich information than current methods.  To piggyback such complex
   and rich information in the data path will consume significant data
   path resource.  But the data path-based method can provide frequently
   changed network information and it is technically simpler to
   synchronize the network information and user data at the same time
   scale.  Normally, there is less user data in the uplink direction and
   the free "space" within the MTU can be used to piggyback the network
   information to the application, without the additional overhead of
   creating a second communication channel between the application and
   network.  However, the data path design may bring out more limited
   privacy management, which is very important in MoWIE.  The
   application cannot trust the network information if there is no
   message authentication mechanism for the piggyback network
   information.  How the network inserts the network information in the
   data packet is also challengeable since a lot of transport layer
   protocol are encrypted and integration protected.  Another method is
   to create an associated path aligned with data path.  Like the ICMP
   for IP and RTCP for RTP, this second path can be used to provide
   additional information associated with the data path.  But creating
   such second path is a big change to current widely used transport
   protocols and a lot of applications also need to change, this second
   path is also challengeable.

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   In this draft, we mainly discuss ALTO extension-based design in
   tackling with this problem.  Specifically, the MoWIE extension will
   reuse existing ALTO mechanisms including information resource
   directory, extensible performance metrics and calendaring, and
   unified properties.  Below is an overview of key considerations;
   security considerations are in the following section.

   *  Network information selection and binding consideration: Instead
      of hardcoding only specific network information, a modular design
      of MoWIE is an ability for an ALTO client to select only the
      relevant information (e.g., cell DLOccupyPRBNum metric and UE MCS)
      and then request correspondingly.  Existing ALTO information
      resource directory is a starting point, but the design needs to be
      generic," to provide abstraction for ease of use and
      extensibility.  The security mechanisms of the existing ALTO
      protocol should also be extended to enforce proper authorization.

   *  Compact network information encoding considerations: One benefit
      of ALTO is its high-level JSON based encoding.  When the update
      frequency increases, the existing base protocol and existing
      extensions (in particular the SSE extension), however, may have
      high bandwidth and processing overhead.  Hence, encoding and
      processing overhead of MoWIE should be considered.

   *  Stability and reliability considerations: A key benefit of the
      MoWIE extension is the ability to allow more flexible, better
      coordinated control.  Any control mechanism, however, should
      integrate fundamental overhead, stability, and reliability
      mechanisms.

   *  Cost metrics considerations.  In [ALTO_METRICS], some cost metrics
      are being standardized including throughput/bit rate, latency,
      priority, error rate, jitter.  These parameters can be linked with
      cost metrics in 5G network entities like network exposure function
      (NEF) [TS23.501] or application function (AF) [TS23.501].  NEF or
      AF, which act as ALTO Clients, utilizing the network information
      exposure capability provided by 3GPP standards, can request to
      expose some of the proposed parameters with consideration of the
      ALTO performance metrics.

   With the better utilization of the 5G network, IETF ALTO can well
   benefit from MoWIE architecture and QoE for the use cases of NAA
   applications can be achieved with the help of the convergence of 5G
   network architecture and IETF ALTO.  For example, it was shown in
   Figure 5-1 that the ALTO client can act as AF or act as part of the
   functional module of AF to converge with the 5G system through the
   implementation in the AF.  It can receive the cellular-based network
   information through interaction with NEF, identify the ALTO server

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   using ALTO service discovery, and request available ALTO information
   prepared by the ALTO server using ALTO Protocol [RFC7285].  The ALTO
   server can be implemented in other places through ALTO architecture
   and can interact with the third parties (e.g.  Content providers) via
   external interfaces.  Furthermore, the ALTO server can aggregate
   information from multiple functional entities to provide an abstract
   and unified view that can be more useful to applications.  Examples
   of other systems include (but are not limited to) static network
   configuration databases, dynamic network information, routing
   protocols, provisioning policies, and interfaces to outside parties
   [RFC7285].  Please note that these components shown in Figure 5-1 are
   out of IETF ALTO scope and are just for completeness to put here.  As
   a result, the ALTO Client can perform ALTO Service Discovery and
   interact with the ALTO server via ALTO Protocol to get the ALTO
   information and ALTO metrics regarding the proposed parameters with
   the help of MoWIE that can be updated dynamically based on network
   conditions from cellular aspects in Section 4.1.  It is noted that in
   addition to AF-based convergence architecture which relies on
   control-plane interfaces e.g.  N5/N33, MoWIE can also utilized user
   plane exposure via N6 or UPF which makes network exposure mechanisms
   more comprehensive and flexisble.

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     +-----------------+  +------------+  +--------------+
     | Dynamic Network |  |  Routing   |  | Provisioning |
     |   Information   |  | protocols  |  |    policy    |
     +---------+-------+  +------+-----+  +------+-------+
               |                 |               |
               +-----------------|---------------+
                                 |
       +-----------+        +---------+
       | External  |        |  ALTO   |
       | Interface |--------| Server  |
       +-----+-----+        +----+----+
             |                   |
             |                   | ALTO protocol
             |                   |
       +-------------+   +-------^--------+
       | 3rd content |   | ALTO Client/AF |
       |   provider  |   +----------------+
       +-------------+           |---------------+
              |                  |               |
              |                  |N33            |N5
              |             +----------+    +---------+
              |             |   NEF    |    |   PCF   |
              |             +----------+    +---------+
              |                  |               |N7
              |                  |---------------+
              |                  |
              |             +----------+ N11 +-------+
              |             |   SMF    |-----|  AMF  |
              |             +----------+     +-------+
              |                  |N4             |N2
              |                  |               |
         +----------+            |               |
         |   Data   |   N6  +-----------+  N3 +--------+   +------+
         |  Network |-------|   UPF     |-----|  RAN   |---|  UE  |
         +----------+       +-----------+     +--------+   +------+
       Figure 5-1: Convergence of 5G network architecture and IETF ALTO

   By extending the exposure scope of network information beyond the
   cellular access, ALTO can help improve the QoE of several
   applications running on endpoints located in cellular networks.
   [ALTO_USE_CASES] is work in progress that investigates use cases
   where the performances of these applications can be further improved
   with abstracted network information and suitable transportation means
   provided by ALTO.  Additionally, upon reviewing the existing ALTO
   capabilities, it lists the ALTO features that need to be extended or
   defined to support the presented use cases.

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6.  IANA Considerations

   This document has no actions for IANA.

7.  Security Considerations

   The collection, distribution of MoWIE information should consider the
   security requirements on information privacy and information
   integration protection and authentication in both sides.  Since the
   network status is not directly related to any special user, there is
   currently no any privacy issue.  But the information transmitted to
   the application can pass through a lot of middle box and can be
   changed by the man in the middle.  To protect the network
   information, an end to end encryption and integration is needed.
   Also, the network needs to authenticate the information exposure
   provided to right applications.  These security requirements can be
   implemented by the TLS and other security mechanisms.

8.  Acknowledgments

   The authors would like to thank Huang Wei and Mohamed Boucadair for
   their contribution and technical review comments to the previous
   versions of this draft.

9.  References

9.1.  Normative References

   [RFC2474]  Nichols, K., Blake, S., Baker, F., and D. Black,
              "Definition of the Differentiated Services Field (DS
              Field) in the IPv4 and IPv6 Headers", RFC 2474,
              DOI 10.17487/RFC2474, December 1998,
              <https://www.rfc-editor.org/info/rfc2474>.

   [RFC3168]  Ramakrishnan, K., Floyd, S., and D. Black, "The Addition
              of Explicit Congestion Notification (ECN) to IP",
              RFC 3168, DOI 10.17487/RFC3168, September 2001,
              <https://www.rfc-editor.org/info/rfc3168>.

   [RFC7285]  Alimi, R., Ed., Penno, R., Ed., Yang, Y., Ed., Kiesel, S.,
              Previdi, S., Roome, W., Shalunov, S., and R. Woundy,
              "Application-Layer Traffic Optimization (ALTO) Protocol",
              RFC 7285, DOI 10.17487/RFC7285, September 2014,
              <https://www.rfc-editor.org/info/rfc7285>.

9.2.  Informative References

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   [ALTO_METRICS]
              "Internet-Draft, draft-ietf-alto-performance-metrics-09",
              " ALTO Performance Cost Metrics", 2020,
              <https://tools.ietf.org/html/draft-ietf-alto-performance-
              metrics-09>.

   [ALTO_USE_CASES]
              "Internet-Draft, draft-li-alto-cellular-use-cases-00",
              " Requirements and reference architecture for Mobile
              Throughput Guidance Exposure", 2021,
              <https://datatracker.ietf.org/doc/html/draft-li-alto-
              cellular-use-cases>.

   [CS2P]     Sun, Yi., Yin, Xiaoqi., Jiang, Junchen., Sekar, Vyas.,
              Lin, Fuyuan., Wang, Nanshu., Liu, Tao., and Bruno.
              Sinopoli, "CS2P: Improving Video Bitrate Selection and
              Adaptation with Data-Driven Throughput Prediction",
              DOI 10.1145/2934872.2934898, 2016,
              <https://doi.org/10.1145/2934872.2934898>.

   [draft-ietf-dmm-5g-uplane-analysis]
              "Internet-Draft, draft-ietf-dmm-5g-uplane-analysis-04",
              " ALTO Uses Cases for Cellular Networks", 2020,
              <https://datatracker.ietf.org/doc/html/draft-ietf-dmm-5g-
              uplane-analysis-04#section-4.1>.

   [ETSI_MEC] "ETSI GS MEC 012", "Multi-access Edge Computing
              (MEC); Radio Network Information API", 2019,
              <https://www.etsi.org/deliver/etsi_gs/MEC/>.

   [Fahad]    Fazal Elahi Guraya, Fahad., Alaya Cheikh, Faouzi., and
              Victor. Medina, "A Novel Visual Saliency Model for
              Surveillance Video Compression",
              DOI 10.1109/SITIS.2011.84, 2011,
              <https://doi.org/10.1109/SITIS.2011.84>.

   [flinck_mobile]
              "Internet-Draft, draft-flinck-mobile-throughput-guidance-
              04", " User Plane Protocol and Architectural Analysis on
              3GPP 5G System", 2017,
              <https://datatracker.ietf.org/doc/html/draft-flinck-
              mobile-throughput-guidance-04>.

   [Hongzi]   Mao, Hongzi., Netravali, Ravi., and Mohammad. Alizadeh,
              "Neural Adaptive Video Streaming with Pensieve",
              DOI 10.1145/3098822.3098843, 2017,
              <https://doi.org/10.1145/3098822.3098843>.

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Authors' Addresses

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   Yuhang Jia
   Tencent
   Flat 9, No. 10 West Building, Xi Bei Wang East Road
   Beijing
   100090
   China
   Email: tonyjia@tencent.com

   Yunfei Zhang
   Tencent
   Flat 9, No. 10 West Building,Xi Bei Wang East Road
   Beijing
   100090
   China
   Email: yanniszhang@tencent.com

   Y. Richard Yang
   Yale University
   Watson 208A, 51 Prospect Street
   New Haven, CT 06511
   United States of America
   Email: yang.r.yang@yale.edu

   Gang Li
   China Mobile Research Institute
   No.32, Xuanwumenxi Ave, Xicheng District
   Beijing
   100053
   China
   Email: ligangyf@chinamobile.com

   Yixue Lei
   Tencent
   Flat 9, No. 10 West Building,Xi Bei Wang East Road
   Beijing
   100090
   China
   Email: yixuelei@tencent.com

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   Yunbo Han
   Tencent
   Tencent Building, No. 10000 Shennan Avenue, Nanshan District
   Shenzhen
   518000
   China
   Email: yunbohan@tencent.com

   S. Randriamasy
   Nokia
   Nokia Bell Labs
   Nozay
   United States of America
   Email: sabine.randriamasy@nokia-bell-labs.com

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