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Versions: 00 01 02 03                                                   
ALTO                                                            C. Xiong
Internet-Draft                                                  Y. Zhang
Intended status: Standards Track                                 Tencent
Expires: January 12, 2022                                        Y. Yang
                                                         Yale University
                                                                   G. Li
                                                                    CMRI
                                                                  Y. Lei
                                                                  Y. Han
                                                                 Tencent
                                                           July 11, 2021


                  MoWIE for Network Aware Application
            draft-huang-alto-mowie-for-network-aware-app-03

Abstract

   With the quick deployment of 5G networks in the world, cloud based
   interactive services such as clouding gaming have gained substantial
   attention and are regarded as potential killer applications.  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 bandwidth and delay experienced by a mobile
   and wireless user can be dynamic, as a function of many factors, and
   unhandled changes can substantially compromise users' QoE.  In this
   document, we investigate network-aware applications (NAA), which
   realize cloud based interactive services with improved QoE, by
   efficient utilization of 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 information, the applications can then
   adapt key control knobs such as media codec scheme, encapsulation and
   application logical function to minimize QoE deduction.  Based on the
   evaluations, we discuss how MoWIE can be a systematic extension 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.

   Internet-Drafts are working documents of the Internet Engineering
   Task Force (IETF).  Note that other groups may also distribute



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   working documents as Internet-Drafts.  The list of current Internet-
   Drafts is at http://datatracker.ietf.org/drafts/current/.

   Internet-Drafts are draft documents valid for a maximum of six months
   and may be updated, replaced, or obsoleted by other documents at any
   time.  It is inappropriate to use Internet-Drafts as reference
   material or to cite them other than as "work in progress."

   This Internet-Draft will expire on January 12, 2022.

Copyright Notice

   Copyright (c) 2021 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
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   include Simplified BSD License text as described in Section 4.e of
   the Trust Legal Provisions and are provided without warranty as
   described in the Simplified BSD License.

Table of Contents

   1.  Introduction of Network-aware Applications  . . . . . . . . .   3
   2.  Use Cases of Network-Aware Application (NAA)  . . . . . . . .   4
     2.1.  Cloud Gaming  . . . . . . . . . . . . . . . . . . . . . .   5
     2.2.  Low Delay Live Show . . . . . . . . . . . . . . . . . . .   5
     2.3.  Cloud VR  . . . . . . . . . . . . . . . . . . . . . . . .   5
     2.4.  Performance Requirements of these Use Cases . . . . . . .   6
   3.  Current (Indirect) Technologies on NAA  . . . . . . . . . . .   6
     3.1.  Video Compression Based on ROI (Region of Interest) . . .   6
     3.2.  AI-based Adaptive Bitrate . . . . . . . . . . . . . . . .   7
   4.  Preliminary QoE Improvement Based on MoWIE  . . . . . . . . .   8
     4.1.  MoWIE Architecture and Network Information exposure . . .   8
     4.2.  RAN assisted TCP optimization based on MoWIE  . . . . . .   9
     4.3.  NAA QoE Test based on MoWIE . . . . . . . . . . . . . . .   9
     4.4.  ROI Detection with Network Information  . . . . . . . . .  10
     4.5.  Adaptive Bitrate with Network Capability Exposure . . . .  13
     4.6.  Analysis of the Experiments . . . . . . . . . . . . . . .  15
   5.  Standardization Considerations of MoWIE as an Extension to
       ALTO  . . . . . . . . . . . . . . . . . . . . . . . . . . . .  16
   6.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  18
   7.  Security Considerations . . . . . . . . . . . . . . . . . . .  18
   8.  Acknowledgments . . . . . . . . . . . . . . . . . . . . . . .  18



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   9.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  18
     9.1.  Normative References  . . . . . . . . . . . . . . . . . .  18
     9.2.  Informative References  . . . . . . . . . . . . . . . . .  18
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  20

1.  Introduction of Network-aware Applications

   With the quick and widely deployment of 5G network in the world, more
   and more applications are now moving to the remote cloud-based
   application, e.g., cloud office, cloud education and cloud gaming.

   Some new and amazing applications are created and hosted in the
   remote cloud, e.g., cloud AR/VR/MR.  What's more a lot of traditional
   niche interactive applications are becoming widely used in daily
   business with the help of mobile network and cloud, e.g., cloud video
   conference.  Especially, during the coronavirus pandemic in 2020,
   many peoples have to stay at home and work/study remotely, the usage
   of cloud applications, including cloud-based online courses, cloud-
   based conferencing, and cloud gaming, has surged significant.

   To provide acceptable QoE to the end users via the mobile network,
   the cloud application needs to know the mobile network status, e.g.,
   delay, bandwidth, jitter to dynamically balance the generated media
   traffic and the rendering/mixing in the cloud.  Currently, the
   application assumes the network as a black box and continuously uses
   client or server measurement to detect the network characteristics,
   and then adaptively change the parameters as well as logical function
   of the application.  However, when only application information is
   utilized, the application can't guarantee a good QoE in some cases.
   First, information from application side may have delay.  When a user
   enters some place with bad network such as elevator or underground
   garage, the application will not receive such 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 can't know how many
   resources it can get and when it will change.  If other users enter
   the cell and compete 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 network layer like
   physical resource block (PRB) information and utilization rate can
   help to describe how many resources the user will get and how many
   users are competing with him.  Such information is helpful to predict
   the network and streaming videos.  However, the application can't get
   those kinds of information yet.

   Mobile network is always pursuing standard solutions to get network
   dynamic indicators that can be used by applications.  In 3GPP, a lot



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   of IP-based QoE mechanism are reused.  The ECN[RFC3168] has been
   supported by the 4G radio station (eNB) to provide CE(Congestion
   Encountered) information to the IMS application to perform the
   Adaptive Bitrate (ABR) [TS26.114].The application can downgrade the
   bit rate after receiving the CE indication, but does not know exact
   bit rate to be selected.  The DSCP[RFC2474] is used to difference the
   QoS class and paging strategy[TS23.501],normally the application
   cannot dynamically change the DSCP to improve bit rate based on the
   network status.  DASH [MPEGDASH] is a MPEG standard widely used for
   the application 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 the re-buffering.  SAND-DASH[TS26.247] defines the mechanism
   that the network/server can provide available throughput to the
   application, in such case, the better bitrate can be selected by DASH
   application.

   In 5G cellular networks, network capability exposure has been
   specified which allows the 5G system to expose the QoS Flow
   establishment with AF provided QoS requirements, user device
   location, network status towards the 3rd party application servers
   modeled as AF (Application Function) [TS23.501].In such case, the AF
   can request the 5G to establish a dedicated QoS Flow to transport an
   IP flow with the AF provided QoS requirements.  The 5G also can
   provide QNC (QoS Notification Control) to the 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 the 5G enhances the QNC with providing a list of
   AQPs(alternative QoS profile). with this AQP, the 5G network provides
   a subset of supported AQPs with the QNC, then the AF selects a bit
   rate from 5G network supported AQPs, in such case, the GBR can
   fulfilled again if the radio state of user is changed.  QoS
   predication 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 towards the entity such as application
   server.  However, both network capability exposure and QoS
   predication solutions are designed for 5G access and core network,
   which cannot cover the whole end-to-end network.  How to enable the
   application to be aware of the lower layer networks in Internet
   scenario is an important area for both industrial and academic
   researchers.

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

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




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2.1.  Cloud Gaming

   As mentioned above, cloud gaming becomes more and more popular
   recently.  This kind of games requires low latency and highly
   reliable transmission of motion tracking data from user to 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 big volume of
   software in the user device), less cost and process load in user
   device and it is also regarded as anti-cheating measure.  Thus, the
   kind of gaming becomes a competitive replacement for console gaming
   using cheaper PC or laptop.  In order to support high quality cloud
   gaming services, the application need 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.

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 the close interaction between application and network.

   Compared with conventional broadcast services.  This service is
   interactive which means the 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 audience, and it also requires
   playing game interaction between the anchor and the audience.  A
   delay lower than 100ms 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 the 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 20ms, the
   application may need to interact with network to get the information
   about the segmentation or transport block information, and these



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   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 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 the lagging significantly
   happens when the latency is larger than 40~200ms, depending on the
   types of games (e.g. 40ms for First Person Shoot games, 80ms for
   Action games, and 200ms for Puzzle games).  In order to avoid bad
   user experiences, the lagging rate is better to be as low as zero (in
   an optimal QoE).  For low latency live show, 20~50 Mbps bandwidth may
   be needed and the end-to-end latency requirements 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 with the parameter settings and they are provided to
   illustrate the order of magnitude of these parameters for the afore-
   mentioned 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, packet loss rate and so on.

   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



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   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 a lot of 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.

   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 25Mbps, which
   brings huge burden to the network, even for 5G network.  Those ROI-
   based video compression methods are mainly applied to the high
   concurrency network 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 the 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



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   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%.

   For this method and those methods built upon this, it has been proven
   that all the information, such as rate, download time, buffer size or
   network level information which can reflect the performance are
   useful to the 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 promote the 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.  Lack of standardized approach to
   acquire these data, is difficult to make this usable for different
   applications for large scale deployment.  Meanwhile, these data which
   reflect the real-time network status change rapidly and randomly
   which is 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 is not rich, direct, real-time, also
   lacks predictability, especially when in the mobile and wireless
   network scenarios, which results in long react 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 the service
   provider to do a better policy control to improve user experience.

   A possible MoWIE architecture include 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 and then further processing on these collected data and
   exposed Network information are provided to the application Server.
   These functions are defined as the network information service
   (NIS)and the NIS can be deployed at MEC (Mobile Edge



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   Computing).  The application server can send the NIS request on 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 influent the application functions
   e.g. bit rate, data amount etc.

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

   Cell level Information:

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

   *  the Downlink MAC data rate per cell;

   *  UE level information (without privacy information):

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

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

   *  The number of packets occupied in PDCP buffer; The number of
      Downlink PDCP SDU packets;

   *  The number of PDCP SDU packets lost;

   *  The Downlink MAC data rate per UE.

4.2.  RAN assisted TCP optimization based on MoWIE

   The RAN information are 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. 100ms) and then piggybacks such information in TCP ACK.

   We have conducted trial in real mobile network.  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



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   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 100ms 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 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.

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



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   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
   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.





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   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.

       +---+-----------------+-----------------+-----------------+
       |   |    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 4-1: 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.



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   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 (eNB) in cellular
   network.

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

   To enable the NAA mechanism, some cellular network information from
   eNBs 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 (Packet Data Convergence Protocol), 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
   eNB, and the eNB 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, 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.




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   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 eNB [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 eNB data are utilized.

   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 4-2: 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.




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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 eNB 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.

   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



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   application information with the eNB 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 eNB
   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

   It should be noticed that the previous mechanisms may also work on
   IEEE 802.11 standards (e.g.  EHT), helping SP having 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 cycle and
   related CQI information (e.g.  MCS, bandwidth, and etc.) 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 datapath.  One issue with
   datapath method is that MoWIE intends to convey more complex and rich
   information than current methods.  To piggyback such complex and rich
   information in the datapath will take away a lot of datapath
   resource.  But the datapath-based method can provide frequent changed
   network information and it is much easy to synchronize the network
   information and user data in 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 informaiton to
   the application, in such case no additional create a second
   communication channel between the application and network.  However,
   the datapath design may bring out more limited privacy management,
   which is very important in MoWIE.  The application cannot trust the



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   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 datapath.  Like the ICMP for IP and RTCP for RTP, this second
   path can be used to provide additional information associated with
   the datapath.  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.

   In 3GPP, network information exposure based on control plane
   mechanism is introduced in 4G and 5G systems.  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.  It also requires modular,
   reusable extensions, which we plan to specify in detail in a separate
   document.  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 consideration: 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 consideration: 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.








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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 for his contribution to the
   previous drafts.

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>.

9.2.  Informative References

   [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>.





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   [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>.

   [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>.

   [MPC]      Yin, Xiaoqi., Jindal, Abhishek., Sekar, Vyas., and Bruno.
              Sigopoli, "A Control-Theoretic Approach for Dynamic
              Adaptive Video Streaming over HTTP", DOI
              10.1145/2785956.2787486, 2015,
              <https://doi.org/10.1145/2785956.2787486>.

   [MPEGDASH]
              ISO/IEC, "ISO/IEC 23009, Dynamic Adaptive Streaming over
              HTTP", 2020, <https://mpeg.chiariglione.org/standards/
              mpeg-dash>.

   [Saccadic]
              Matin, E., "Saccadic suppression: A review and an
              analysis", DOI 10.1037/h0037368, 1974,
              <https://doi.org/10.1037/h0037368>.

   [Saliency]
              Guo, C. and L. Zhang, "A Novel Multiresolution
              Spatiotemporal Saliency Detection Model and Its
              Applications in Image and Video Compression", DOI 10.1109/
              TIP.2009.2030969, 2017, <https://doi.org/10.1109/
              TIP.2009.2030969>.

   [TS23.501]
              "3rd Generation Partnership Project (3GPP)", "System
              architecture for the 5G System (5GS)", 2021,
              <https://portal.3gpp.org/desktopmodules/Specifications/
              SpecificationDetails.aspx?specificationId=3144>.

   [TS26.114]
              "3rd Generation Partnership Project (3GPP)", "IP
              Multimedia Subsystem (IMS); Multimedia telephony; Media
              handling and interaction", 2021,
              <https://portal.3gpp.org/desktopmodules/Specifications/
              SpecificationDetails.aspx?specificationId=1404>.





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   [TS26.247]
              "3rd Generation Partnership Project (3GPP)", "Progressive
              Download and Dynamic Adaptive Streaming over HTTP(3GP-
              DASH)", 2020,
              <https://portal.3gpp.org/desktopmodules/Specifications/
              SpecificationDetails.aspx?specificationId=1444>.

   [TS38.214]
              "3rd Generation Partnership Project (3GPP)", "NR; Physical
              layer procedures for data", 2021,
              <https://portal.3gpp.org/desktopmodules/Specifications/
              SpecificationDetails.aspx?specificationId=3216>.

Authors' Addresses

   Chunshan Xiong
   Tencent
   Flat 9, No. 10 West Building, Xi Bei Wang East Road
   Beijing
   100090
   China

   Email: chunshxiong@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









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   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


   Yunbo Han
   Tencent
   Tencent Building, No. 10000 Shennan Avenue, Nanshan District
   Shenzhen
   518000
   China

   Email: yunbohan@tencent.com























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