MoWIE for Network Aware Application
draft-huang-alto-mowie-for-network-aware-app-00
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| Authors | Wei Huang , Yunfei Zhang , Richard Yang , Chunshan Xiong , Yixue Lei , Yunbo Han , Gang Li | ||
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draft-huang-alto-mowie-for-network-aware-app-00
ALTO W. Huang
Internet Draft Y. Zhang
Intended status: Proposed Standard Tencent
Expires: September 2020 R.Yang
Yale University
C. Xiong
Y. Lei
Y. Han
Tencent
G. Li
CMRI
March 10, 2020
MoWIE for Network Aware Application
draft-huang-alto-mowie-for-network-aware-app-00
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.
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Legal Provisions and are provided without warranty as described in
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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)....... 7
3.2. AI-based Adaptive Bitrate................................. 7
4. Preliminary Improvement Based on MoWIE........................ 9
4.1. ROI Detection with Network Information.................... 9
4.2. Adaptive Bitrate with Network Capability Exposure........ 13
4.3. Analysis of the Experiments.............................. 15
5. Standardization Considerations of MoWIE as an Extension to ALTO16
6. Security Considerations...................................... 18
7. References................................................... 18
7.1. Normative References..................................... 18
7.2. Informative References................................... 19
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 5G and cloud, e.g., cloud video
conference. Take AAA cloud gaming which needs a lot of CPU and GPU
for example, the edge cloud (e.g., MEC in 5G)performs the media
rendering and mixing and only provides the processed media stream to
the client, and the slim client only need to decode and display the
visual content with imperceptible delay introduced by 5G network.
The player feels just like executing all tasks in the client as
before. To provide acceptable QoE to the end users, the cloud
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application needs to know the 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, these application layer
mechanisms may work very well in some networks but do not work well
in other networks, e.g., the cellular network. A lot of re-buffering
and reconnection makes the QoE even worse, e.g., some pictures are
blurry, and some pictures are skipped.
Mobile network is always pursuing standard solutions to get network
dynamic indicators that can be used by applications step by step. In
3GPP, the ECN has been supported by the 4G radio station (eNB) to
provide congestion information to the IMS application to perform the
Adaptive Bitrate (ABR) [TS26.114].
DASH [MPEG DASH] is a MPEG standard widely used 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.
If the network can provide more information such as the guaranteed
throughput to the application, the better bitrate will be selected by
DASH.
In 5G cellular networks, network capability exposure has been
specified which allows the 5G system to expose the user device
location, network status towards the 3rd party application servers
modeled as AF (Application Function) [TS23.501]. However, this only
works 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
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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
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 [GSMA].
With the typical parameters setting, cloud gaming generally needs a
bandwidth of 20~60 Mbps and an end to end delay of 30~70ms. In cloud
gaming, we consider the lagging happens when the latency is larger
than 200ms. 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
There are a lot of technologies on NAA, such as buffer control
method, adaptive bit rate method and so on. However, 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.
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3.1. Video Compression Based on ROI (Region of Interest)
A foveated mechanism [Saccadic] in the Human Visual System (HVS)
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).
To predict human attention or ROI, saliency detection has been widely
studied in recent years [Borji], 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].
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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 [Fahad] 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%.
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 [Hongzi2]. 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. Lacking 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, and
also lacks of predictability, especially when in the mobile and
wireless network scenarios, which results in long react delay or high
QoE fluctuations.
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4. Preliminary Improvement 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 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.
4.1. 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.
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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 [Minbarrier].
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 [LSTM].
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
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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 4-1: 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.
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.
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4.2. 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.
The collected cell level information is:
- DLOccupyPRBNum: The number of Downlink PRBs(Physical Resource
Block) occupied during sampling
- CellDLMACRate: The Downlink MAC data rate per cell
UE level information includes:
- ULSINR: The Uplink SINR (Signal to Inference plus Noise Ratio)
- MCS: The index of MCS (Modulation and Coding Scheme)
- PDCPOccupBuffer: The number of packets occupied in PDCP buffer
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- DLPDCPSDUNum: The number of Downlink PDCP SDU packets
- DLPDCPLossNum: The number of PDCP SDU packets lost
- DLMACRate: The Downlink MAC data rate per UE
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 1~9 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 3-9: Other scenarios with random user movement trace
and user distribution.
Test method: To simplify to comparison, we just use two information
derived from the eNB including the MCS (MCS index) and PRB
(DLOccupyPRBNum) [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
number of Downlink PRBs occupied shows the capacity used in the cell,
which helps to predict the traffic of network in heavy or light load.
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, 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.
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+-------------+--------------------------+
|Test Scenario| Reduction of Lagging Rate|
+-------------+--------------------------+
| 1 | 46% |
+-------------+--------------------------+
| 2 | 21% |
+-------------+--------------------------+
| 3 | 37% |
+-------------+--------------------------+
| 4 | 56% |
+-------------+--------------------------+
| 5 | 32% |
+-------------+--------------------------+
| 6 | 67% |
+-------------+--------------------------+
| 7 | 33% |
+-------------+--------------------------+
| 8 | 57% |
+-------------+--------------------------+
| 9 | 48% |
+-------------+--------------------------+
Figure 4-2: Reduction of Lagging Rate
It can be clearly seen that with the MCS and PRB 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. And the performance gain can even
reach to 67% in some scenario.
4.3. 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.
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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.
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 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
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
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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. Moreover the datapath design may bring out more limited
privacy management, which is very important in MoWIE. 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
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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. .
6. 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.
7. References
7.1. Normative References
[Fahad] Fahad Fazal Elahi Guraya ; Faouzi Alaya Cheikh ; Victor
Medina; A Novel Visual Saliency Model for Surveillance
Video Compression, 2011 Seventh International Conference
on Signal Image Technology & Internet-Based Systems
[Hongzi] Hongzi Mao; Ravi Netravali; Mohammad Alizadeh; Neural
Adaptive Video Streaming with Pensieve; SIGCOMM '17:
Proceedings of the Conference of the ACM Special Interest
Group on Data Communication; August 2017 Pages 197-210
[Saccadic] E. Matin, Saccadic suppression: a review and an
analysis, Psychological bulletin 81 (12) (1974) 899-917.
[Borji] A. Borji, L. Itti, State-of-the-art Analysis and Machine
Intelligence, IEEE Transactions on 35 (1) (2013) 185-
207.
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[MPC] X. Yin, A. Jindal, V. Sekar, and B. Sinopoli. 2015. A
Control-Theoretic Approach for Dynamic Adaptive Video
Streaming over HTTP. In SIGCOMM. ACM.
[CS2P] Y. Sun et al. 2016. CS2P: Improving Video Bitrate Selection
and Adaptation with Data-Driven Throughput Prediction. In
SIGCOMM. ACM.
[Hongzi2] Hongzi Mao, Shannon Chen, Drew Dimmery, Shaun Singh, Drew
Blaisdell, Yuandong Tian, Mohammad Alizadeh, Eytan Bakshy;
Real-world Video Adaptation with Reinforcement Learning ;
ICML 2 2019 Workshop RL4RealLife
[Saliency] Chenlei Guo, Liming Zhang; A Novel Multiresolution
Spatiotemporal Saliency Detection Model and Its
Applications in Image and Video Compression, IEEE
TRANSACTIONS ON IMAGE PROCESSING, VOL. 19, NO. 1, JANUARY
2010
[Minbarrier] Jianming Zhang, Stan Sclaroff, Zhe Lin, Xiaohui Shen,
Brian Price, Radomir Mech; Minimum barrier salient object
detection at 80 fps. The IEEE International Conference on
Computer Vision (ICCV), 2015, pp. 1404-1412.
[LSTM] Lai Jiang; Mai Xu; Zulin Wang; Predicting video Saliency
with Object-to-Motion CNN and Two-layer Convolutional
LSTM, arXiv:1709.06316v3 [cs.CV] 14 Jan 2019
7.2. Informative References
[TS23.501] 3GPP TS 23.501 System architecture for the 5G System
(5GS), http://www.3gpp.org/ftp//Specs/archive/23_ser
ies/23.501/23501-g30.zip
[TS38.214] 3GPP TS 38.214, NR Physical layer procedures for data,
http://www.3gpp.org/ftp//Specs/archive/38_series/38.214/38
214-g00.zip
[TS26.114] 3GPP TS 26.114, IP Multimedia Subsystem (IMS);
Multimedia telephony; Media handling and interaction,
http://www.3gpp.org/ftp//Specs/archive/26_series/26.114/26
114-g40.zip
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[MPEG DASH]ISO/IEC 23009, Dynamic Adaptive Streaming over HTTP;
https://mpeg.chiariglione.org/standards/mpeg-dash
[iiMedia] 2019-2020 China Online Live Streaming Market Research
Report, https://www.iimedia.cn/c400/69017.html
[GSMA] Cloud AR/VR Whitepaper, Last updated on April 26, 2019,
https://www.gsma.com/futurenetworks/wiki/cloud-ar-vr-
whitepaper/#
Authors' Addresses
Wei Huang
Tencent Building,
No. 10000 Shennan Avenue, Nanshan District
Shenzhen, Guangdong, 518000
China
Email: wienhuang@tencent.com
Yunfei Zhang
Flat 9, No. 10 West Building.
Xi Bei Wang East Road
Beijing, 100090
China
Email: yanniszhang@tencent.com
Y. Richard Yang
Watson 208A,
51 Prospect Street
New Haven, CT 06511
USA
Email: yang.r.yang@yale.edu
Chunshan Xiong
Flat 9, No. 10 West Building.
Xi Bei Wang East Road
Beijing, 100090
China
Email: chunshxiong@tencent.com
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Yixue Lei
Flat 9, No. 10 West Building.
Xi Bei Wang East Road
Beijing, 100090
China
Email: yixuelei@tencent.com
Yunbo Han
Tencent Building,
No. 10000 Shennan Avenue, Nanshan District
Shenzhen, Guangdong, 518000
China
Email: yunbohan@tencent.com
Gang Li
China Mobile Research Institute
No.32, Xuanwumenxi Ave, Xicheng District
Beijing 100053,
China
Email:ligangyf@chinamobile.com
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