MoWIE for Network Aware Application
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|Authors||Wei Huang , Yunfei Zhang , Richard Yang , Chunshan Xiong , Yixue Lei , Yunbo Han , Gang Li|
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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. Huang Expires September 10, 2020 [Page 1] Internet-Draft MoWIE for Network Aware Application March 2020 Status of this Memo This Internet-Draft is submitted to IETF in full conformance with the provisions of BCP 78 and BCP 79. Internet-Drafts are working documents of the Internet Engineering Task Force (IETF), its areas, and its working groups. Note that other groups may also distribute working documents as Internet- Drafts. The list of current Internet-Drafts is at https://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." The list of current Internet-Drafts can be accessed at https://www.ietf.org/1id-abstracts.html The list of Internet-Draft Shadow Directories can be accessed at https://www.ietf.org/shadow.html Copyright and License Notice Copyright (c) 2020 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 Provisions Relating to IETF Documents (https://trustee.ietf.org/license-info) in effect on the date of publication of this document. Please review these documents carefully, as they describe your rights and restrictions with respect to this document. Please review these documents carefully, as they describe your rights and restrictions with respect to this document. Code Components extracted from this document must include Simplified BSD License text as described in Section 4.e of the Trust Huang Expires September 10, 2020 [Page 2] Internet-Draft MoWIE for Network Aware Application March 2020 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)....... 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 Huang Expires September 10, 2020 [Page 3] Internet-Draft MoWIE for Network Aware Application March 2020 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. Huang Expires September 10, 2020 [Page 4] Internet-Draft MoWIE for Network Aware Application March 2020 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 Huang Expires September 10, 2020 [Page 5] Internet-Draft MoWIE for Network Aware Application March 2020 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. Huang Expires September 10, 2020 [Page 6] Internet-Draft MoWIE for Network Aware Application March 2020 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]. Huang Expires September 10, 2020 [Page 7] Internet-Draft MoWIE for Network Aware Application March 2020 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. Huang Expires September 10, 2020 [Page 8] Internet-Draft MoWIE for Network Aware Application March 2020 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. Huang Expires September 10, 2020 [Page 9] Internet-Draft MoWIE for Network Aware Application March 2020 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 Huang Expires September 10, 2020 [Page 10] Internet-Draft MoWIE for Network Aware Application March 2020 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. Huang Expires September 10, 2020 [Page 11] Internet-Draft MoWIE for Network Aware Application March 2020 +---+-----------------+-----------------+-----------------+ | | 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. Huang Expires September 10, 2020 [Page 12] Internet-Draft MoWIE for Network Aware Application March 2020 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 Huang Expires September 10, 2020 [Page 13] Internet-Draft MoWIE for Network Aware Application March 2020 - 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. Huang Expires September 10, 2020 [Page 14] Internet-Draft MoWIE for Network Aware Application March 2020 +-------------+--------------------------+ |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. Huang Expires September 10, 2020 [Page 15] Internet-Draft MoWIE for Network Aware Application March 2020 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 Huang Expires September 10, 2020 [Page 16] Internet-Draft MoWIE for Network Aware Application March 2020 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 Huang Expires September 10, 2020 [Page 17] Internet-Draft MoWIE for Network Aware Application March 2020 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. Huang Expires September 10, 2020 [Page 18] Internet-Draft MoWIE for Network Aware Application March 2020 [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 Huang Expires September 10, 2020 [Page 19] Internet-Draft MoWIE for Network Aware Application March 2020 [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: email@example.com Yunfei Zhang Flat 9, No. 10 West Building. Xi Bei Wang East Road Beijing, 100090 China Email: firstname.lastname@example.org Y. Richard Yang Watson 208A, 51 Prospect Street New Haven, CT 06511 USA Email: email@example.com Chunshan Xiong Flat 9, No. 10 West Building. Xi Bei Wang East Road Beijing, 100090 China Email: firstname.lastname@example.org Huang Expires September 10, 2020 [Page 20] Internet-Draft MoWIE for Network Aware Application March 2020 Yixue Lei Flat 9, No. 10 West Building. Xi Bei Wang East Road Beijing, 100090 China Email: email@example.com Yunbo Han Tencent Building, No. 10000 Shennan Avenue, Nanshan District Shenzhen, Guangdong, 518000 China Email: firstname.lastname@example.org Gang Li China Mobile Research Institute No.32, Xuanwumenxi Ave, Xicheng District Beijing 100053, China Email:email@example.com Huang Expires September 10, 2020 [Page 21]