Packet scheduling with playout adaptation for scalable video delivery over wireless networks

In this paper, we propose a playout deadline-aware packet scheduling for scalable video delivery over wireless networks. We develop a novel playout adaptation algorithm to reduce playback interruptions by jointly considering the active playout buffer status and adaptive playout rate. We also propose a packet priority analysis method based on the layer information of Scalable Video Coding (SVC). Based on the priority of the video packet and the adaptive playout-deadline, an optimal packet scheduling algorithm is proposed. Packets are selected for transmission to minimize the quality degradation caused as well as to reduce the playout latency. We also adopt a benchmark for the packet priority analysis by calculating the distortion impact of each packet with the consideration of the packet dependency in SVC. When compared with the state-of-the-art algorithms as well as the benchmark, our proposed scheduling algorithm shows a good trade-off between the video quality and the playout latency.     

基于多终端协同的高清视频多流并发自适应传输控制方法

摘要：针对高清视频在异构无线网络中以多流并发的方式进行传输,以提高传输速率,从而增强用户体验的问题,以最小化系统传输时延以及各路径间时延差为优化目标,联合考虑了视频发送端和接收端,自适应调整视频发送速率和接收端缓存大小以提高用户体验,建立了异构无线网络中视频多流并发传输的控制模型,并基于Pareto分布和P/P/1排队理论对具有自相似性和长相关性的视频流进行了研究,推导了并发传输系统的时延统计特性,并在此基础上提出了一种异构无线网络视频流自适应分流决策方法。仿真结果表明,与一般的负载均衡分流决策方法相比,提出的异构网络多流并发自适应传输控制方法在时延和分组丢失率方面都有一定的优越性。     

A brief survey on adaptive video streaming quality assessment

Quality of experience (QoE) assessment for adaptive video streaming plays a significant role in advanced network management systems. It is especially challenging in case of dynamic adaptive streaming schemes over HTTP (DASH) which has increasingly complex characteristics including additional playback issues. In this paper, we provide a brief overview of adaptive video streaming quality assessment. Upon our review of related works, we analyze and compare different variations of objective QoE assessment models with or without using machine learning techniques for adaptive video streaming. Through the performance analysis, we observe that hybrid models perform better than both quality-of-service (QoS) driven QoE approaches and signal fidelity measurement. Moreover, the machine learning-based model slightly outperforms the model without using machine learning for the same setting. In addition, we find that existing video streaming QoE assessment models still have limited performance, which makes it difficult to be applied in practical communication systems. Therefore, based on the success of deep learned feature representations for traditional video quality prediction, we also apply the off-the-shelf deep convolutional neural network (DCNN) to evaluate the perceptual quality of streaming videos, where the spatio-temporal properties of streaming videos are taken into consideration. Experiments demonstrate its superiority, which sheds light on the future development of specifically designed deep learning frameworks for adaptive video streaming quality assessment. We believe this survey can serve as a guideline for QoE assessment of adaptive video streaming.     

A novel adaptive logic for dynamic adaptive streaming over HTTP

In this paper, we propose an estimation method that estimates the throughput of upcoming video segments based on variations in the network throughput observed during the download of previous video segments. Then, we propose a rate-adaptive algorithm for Hypertext Transfer Protocol (HTTP) streaming. The proposed algorithm selects the quality of the video based on the estimated throughput and playback buffer occupancy. The proposed method selects high-quality video segments, while minimizing video quality changes and the risk of playback interruption, improving user’s experience. We evaluate the algorithm for single- and multi-user environments and demonstrate that it performs remarkably well under varying network conditions. Furthermore, we determine that it efficiently utilizes network resources to achieve a high video rate; competing HTTP clients achieve equitable video rates. We also confirm that variations in the playback buffer size and segment duration do not affect the performance of the proposed algorithm.     

基于TCP协议的音频流媒体QoE的定量研究

流媒体接收端音频的初始延时或中断,以及采用自适应播放产生的播放速率变化是基于TCP协议的音频流媒体的用户体验质量下降的主要原因。介绍了一种中断强度的概念,并通过模拟实际网络环境的主观音质测试方法来分析中断强度对QoE的影响。结果表明,在可用带宽发生变化时,中断强度和主观音质测试得分有很好的相关性,平均相关系数为-0.9。从而表明中断强度可以作为评价音频流媒体QoE的客观评价参数。此外,还对播放速率变化对QoE的影响进行了主观音质测试,变速算法为波形相似叠加算法。测试结果显示,当变速因子超过105%时,音质下降严重,测试结果可以作为自适应播放中参数设置的参考。     

Software‐defined network‐based prioritization to avoid video freezes in HTTP adaptive streaming

HTTP adaptive streaming (HAS) is becoming the de facto standard for video streaming services over the Internet. In HAS, each video is segmented and stored in different qualities. Rate adaptation heuristics, deployed at the client, allow the most appropriate quality level to be dynamically requested, based on the current network conditions. It has been shown that state‐of‐the‐art heuristics perform suboptimal when sudden bandwidth drops occur, therefore leading to freezes in the video playout, the main factor influencing users' quality of experience (QoE). This issue is aggravated in case of live events, where the client‐side buffer has to be kept as small as possible in order to reduce the playout delay between the user and the live signal. In this article, we propose a framework capable of increasing the QoE of HAS clients by reducing video freezes. The framework is based on OpenFlow, a widely adopted protocol to implement the software‐defined networking principle. An OpenFlow controller is in charge of introducing prioritized delivery of HAS segments, based on the network conditions and the HAS clients' status. Particularly, the HAS clients' status is obtained without any explicit clients‐to‐controller communication, and thus, no extra signaling is introduced into the network. Moreover, this OpenFlow controller is transparent to the quality decision process of the clients, as it assists the delivery of the segments, but it does not determine the actual quality to be requested. In order to provide a comprehensive analysis of the proposed approach, we investigate the performance of the proposed OpenFlow‐based framework in the presence of realistic Internet cross‐traffic. Particularly, we model two types of applications, namely, HTTP web browsing and progressive download video streaming, which currently represent the majority of Internet traffic together with HAS. By evaluating this novel approach through emulation in several multi‐client scenarios, we show how the proposed approach can reduce freeze time for the HAS clients due to network congestion up to 10 times compared with state‐of‐the‐art heuristics, without impacting the performance of the cross‐traffic applications. Copyright © 2016 John Wiley & Sons, Ltd.     

突发大时延下的IP语音缓冲算法

当网络中存在突发大时延时,就会出现极大丢包率或极大端到端时延的情况,从而难以获得好的语音质量。对于这个问题,提出针对突发大时延存在下的自适应语音缓冲算法。算法通过控制语音包在语音缓冲队列中的位置来控制语音包的播放时间,从而可以尽量减小语音裂缝(Gap)的出现。算法将突发大时延存在下的最大丢包率可以扩大到20％,而一般的预测算法只能容忍5-10％的最大丢包率。通过基于听觉模型的客观音质评价(PESQ)仿真计算,以及实际应用表明本文的算法对有突发大时延存在的网络中的语音通信质量有一定的改善作用。     

TRASS: A transmission rate‐adapted streaming server in a wireless environment

The video streaming quality in a wireless communication network environment is largely affected by various network characteristics, such as a limited channel bandwidth and a variant transmission rate. The playback quality of User Equipments (UEs) may not be smooth when the service is delivered via a wireless environment. From the viewpoints of most video receivers, a smooth playback with a lower video quality may be more significant than a lagged or distorted playback with a higher video quality as the transmission rate degrades. Based on the above, we sketch an adaptation agent—Transmission‐Rate Adapted Streaming Server (TRASS), which is located between the original video server and UEs, to adaptively transform the streaming video based on the real transmission rate. In our proposed scheme, UEs would feedback their network access statuses to TRASS and then TRASS would deliver adaptive quality of video streams to UEs according to their feedbacks. The theoretical analysis and simulations using different video tracks encoded in MPEG‐4 and H.264/AVC formats show that TRASS can help wireless streaming users to get a smooth playback quality with a lower packet failure rate. With a low probability of receiving a worse quality of video, users' Quality of Experience can subsequently be raised. Copyright © 2010 John Wiley & Sons, Ltd.     

Research on Q-learning based rate control approach for HTTP adaptive streaming

HTTP adaptive streaming (HAS) has become the standard for adaptive video streaming service.In changing network environments,current hardcoded-based rate adaptation algorithm was less flexible,and it is insufficient to consider the quality of experience (QoE).To optimize the QoE of users,a rate control approach based on Q-learning strategy was proposed.the client environments of HTTP adaptive video streaming was modeled and the state transition rule was defined.Three parameters related to QoE were quantified and a novel reward function was constructed.The experiments were employed by the Q-learning rate control approach in two typical HAS algorithms.The experiments show the rate control approach can enhance the stability of rate switching in HAS clients.     

Segment‐aware dynamic routing for DASH flows over software‐defined networks

Most of the video streaming applications running over the Internet send video data over HTTP and provide an architecture for video clients to adapt video quality during streaming. In HTTP adaptive streaming, a raw video is encoded at various qualities, each encoded video file is divided into small segments, and the clients may change the segment quality by sending requests for segments having different qualities over time. MPEG has standardized dynamic adaptive streaming over HTTP (MPEG‐DASH) due to this tendency. In this work, we focus on DASH over software‐defined networks (SDN), and we dynamically reroute DASH flows by considering the current network capacity, available bandwidth of the paths, and bitrate of the segments in order to provide high quality of experience (QoE) and fairness among DASH clients. Simulations performed under various network conditions show that the proposed study provides higher QoE and fairness compared with the max‐flow routing approach.     

QoE in video streaming over wireless networks: perspectives and research challenges

The deployment of 3G/LTE networks and advancements in smart mobile devices had led to high demand for multimedia streaming over wireless network. The rapid increasing demand for multimedia content poses challenges for all parties in a multimedia streaming system, namely, content providers, wireless network service providers, and smart device makers. Content providers and mobile network service providers are both striving to improve their streaming services while utilizing advancing technologies. Smart device makers endeavor to improve processing power and displays for better viewing experience. Ultimately, the common goal shared by content providers, network service providers, and smart device manufactures is to improve the QoE for users. QoE is both an objective and a subjective metric measuring the streaming quality experience by end users. It may be measured by streaming bitrate, playback smoothness, video quality metrics like Peak to Signal Noise Ratio, and other user satisfaction factors. There have been efforts made to improve the streaming experiences in all these aspects. In this paper, we conducted a survey on existing literatures on QoE of video streaming to gain a deeper and more complete understanding of QoE quality metrics. The goal is to inspire new research directions in defining better QoE and improving QoE in existing and new streaming services such as adaptive streaming and 3D video streaming.     

An adaptive retransmission‐based multipath transmission mechanism for conversational video

Conversational video service is characterized by high bandwidth demand and low delay requirement. Bandwidth and transmission schemes play an important role in providing high‐quality delivery service for point‐to‐point conversational video service. Multipath transmission is regarded as an effective way to aggregate bandwidth. Transmission schemes need to ensure the strict time relation between information entities and to alleviate the negative impact of packet loss on video quality. To achieve this, existing transmission schemes may incur either a large delay or a large amount of duplicated packets that are not suitable for conversational video service. In this paper, we propose an adaptive retransmission mechanism–based multipath transmission (MT‐AR) for conversational video service delivery. MT‐AR takes advantage of historical reception experience to timely detect packet loss with a certain degree of misjudgement. Receiver requests sender to retransmit the lost packet if the lost packet benefits the decoding. Adaptive playout speed adjustment and alternative path retransmission cooperatively optimize the performance of retransmission. Receiver slightly extends playout speed to reserve time for retransmission and accelerates playout speed to alleviate negative impact of cumulative extension. Multiple paths support to conduct retransmission on an optimal path selected from alternative paths to avoid continuous congestion or error on the original path. Finally, we conduct extensive tests to evaluate the performance of MT‐AR. Experimental results show that MT‐AR can effectively improve the quality of experience of conversational video service by retransmission.     

QoE optimization for HTTP adaptive streaming: Performance evaluation of MEC-assisted and client-based methods

Seamless streaming of high quality video under unstable network condition is a big challenge. HTTP adaptive streaming (HAS) provides a solution that adapts the video quality according to the network conditions. Traditionally, HAS algorithm runs at the client side while the clients are unaware of bottlenecks in the radio channel and competing clients. The traditional adaptation strategies do not explicitly coordinate between the clients, servers, and cellular networks. The lack of coordination has been shown to lead to suboptimal user experience. As a response, multi-access edge computing (MEC)-assisted adaptation techniques emerged to take advantage of computing and content storage capabilities in mobile networks. In this study, we investigate the performance of both MEC-assisted and client-side adaptation methods in a multi-client cellular environment. Evaluation and comparison are performed in terms of not only the video rate and dynamics of the playback buffer but also the fairness and bandwidth utilization. We conduct extensive experiments to evaluate the algorithms under varying client, server, dataset, and network settings. Results demonstrate that the MEC-assisted algorithms improve fairness and bandwidth utilization compared to the client-based algorithms for most settings. They also reveal that the buffer-based algorithms achieve significant quality of experience; however, these algorithms perform poorly compared with throughput-based algorithms in protecting the playback buffer under rapidly varying bandwidth fluctuations. In addition, we observe that the preparation of the representation sets affects the performance of the algorithms, as does the playback buffer size and segment duration. Finally, we provide suggestions based on the behaviors of the algorithms in a multi-client environment.     

自适应流媒体技术在CDN中的应用研究

内容分发网络(CDN)面临多终端、多业务融合所带来的各种技术挑战,自适应流媒体技术是一种结合了流控和HTTP渐进式下载的分发技术,它能够自动适应网络带宽,为用户提供良好的视频播放体验。论述CDN平台承载自适应流媒体业务所面临的技术挑战,并提出相应的应用解决方案,提高用户的视频服务体验,满足不断发展的视频服务需求。     

A retransmission‐based scheme for video streaming over wireless channels

Efficient resource allocation is a key factor to improve the efficiency of video transmission over wireless channels. To increase the number of correctly received video frames at the decoder, it is desirable to reduce the video source rate while increasing error protection when the wireless channel is anticipated to be bad or when the receiver buffer is approaching starvation. In this study, we introduce a retransmission‐based adaptive source‐channel rate control scheme for video transmission over wireless packet networks. In this scheme, the level of adaptiveness is optimized to reduce the bandwidth requirement while guaranteeing delay and loss bounds. The proposed scheme has the advantage of providing closed‐form expressions of the near‐optimum parameters of the proposed model, which are then fed back to the transmitter to scale both the source and channel rates adaptively. Simulation and numerical investigations are carried out to verify the adequacy of the analysis and study the impact of the adaptive process on the continuity of the video playback process. Copyright © 2009 John Wiley & Sons, Ltd.     

Radio Link Buffer Management and Scheduling for Wireless Video Streaming

In this paper we compare strategies for joint radio link buffer management and scheduling for wireless video streaming. Based on previous work in this area [8], we search for an optimal combination of scheduler and drop strategy for different end-to-end streaming options including timestamp-based streaming and ahead-of-time streaming, both with variable initial playout delay. We will show that a performance gain versus the two best drop strategies in Liebl et al. [8], i.e. drop the HOL packet or drop the packet with the lowest priority starting from HOL, is possible: Provided that some basic side-information on the structure of the incoming video stream is available, a more sophisticated drop strategy removes packets from an HOL group of packets in such a way that the temporal dependencies usually present in video streams are not violated. This advanced buffer management scheme yields significant improvements for almost all investigated scheduling algorithms and streaming options. In addition, we will demonstrate the importance of fairness among users when selecting a suitable scheduler, especially if ahead-of-time streaming is to be applied: Given a reasonable initial playout delay at the streaming media client, both the overall achievable quality averaged over all users, as well as the individual quality of users with bad channel conditions can be increased significantly by trading off fairness with maximum throughput of the system.     

Shielding video streaming against packet losses over VANETs

Vehicular ad-hoc networks (VANETs) are being widely adopted in the last few years. This type of network enables the utilization of a large diversity of distributed applications, such as road and traffic alerts, autonomous driving capabilities and video distribution. Video applications can be considered one of the most demanding services because it needs a steady and continuous flow of information. This presents a set of challenges to VANETs considering their scarce network resources due to the vehicle movement and time-varying wireless channels. Considering the above mentioned issues, an adaptive quality of experience (QoE)-driven mechanism is needed to provide live transmission capabilities to video-equipped vehicles. This mechanism has to overcome the challenges to grant a high-quality video transmission without adding any unnecessary network overhead. To this end, a forward error correction (FEC) technique can be adapted to enhance the video distribution, leading to higher QoE for end users. The proposed self-adaptive FEC-based mechanism (SHIELD) uses several video characteristics and specific VANETs details to safeguard real-time video streams against packet losses. One of the main contributions of this work is the combined used of network density, signal-to-noise ratio, packet loss rate, and the vehicle’s position. This allows SHIELD to better protect the video sequences and enhance the QoE. In doing that, we are able to improve the user experience, while saving network resources. The advantages and drawbacks of the proposed mechanism are demonstrated through extensive experiments and assessed with QoE metrics, proving that it outperforms both adaptive and non-adaptive mechanisms.     

HTTP自适应流媒体直播系统中的用户体验质量优化

分析HTTP自适应流媒体直播系统中对终端用户体验质量(QoE)产生影响的各类因素及其相互之间的作用关系,对基于服务器端、网络传输以及客户端的QoE优化策略进行总结。认为HTTP自适应流媒体直播系统的QoE优化重点在于降低延时,提出结合网络层和应用层影响因素来降低时延并提升用户QoE的建议。     

Impact of VNF placements on QoE monitoring in the cloud

The fast growth of video streaming is responsible for a huge amount of traffic over the past few years. Because of the variety of video content on the Internet, a potential market is emerging for video providers. However, a rapid increase in video traffic and users poses challenges for network operators maintaining user expectation. To address this problem, network operators need a mechanism to monitor the quality of experience (QoE) perceived by the user. This allows reacting to quality degradation to improve the service. With the paradigm of network function virtualization, network operators are able to deploy such a video monitoring function in the cloud. In this work, we investigate the feasibility of deploying a virtual network function (VNF) for video buffer and QoE monitoring on the Amazon Web Service cloud. To this end, we implement a VNF to analyze video flows in the network by using deep packet inspection. We investigate the influence of different points of presence (PoP) in the cloud and mobile network on the performance of the VNF for monitoring video buffer and QoE. Our findings show that the accuracy of the QoE monitoring decreases with the distance of the PoP to the client. This is not only due to the delay and bottleneck between the monitoring point and the client but also due to the client mobile access network.     

TCP-friendly flow control of wireless multimedia using ECN marking

In a wireless network packet losses can be caused not only by network congestion but also by unreliable error-prone wireless links. Therefore, flow control schemes which use packet loss as a congestion measure cannot be directly applicable to a wireless network because there is no way to distinguish congestion losses from wireless losses. In this paper, we extend the so-called TCP-friendly flow control scheme, which was originally developed for the flow control of multimedia flows in a wired IP network environment, to a wireless environment. The main idea behind our scheme is that by using explicit congestion notification (ECN) marking in conjunction with random early detection (RED) queue management scheme intelligently, it is possible that not only the degree of network congestion is notified to multimedia sources explicitly in the form of ECN-marked packet probability but also wireless losses are hidden from multimedia sources. We calculate TCP-friendly rate based on ECN-marked packet probability instead of packet loss probability, thereby effectively eliminating the effect of wireless losses in flow control and thus preventing throughput degradation of multimedia flows travelling through wireless links. In addition, we refine the well-known TCP throughput model which establishes TCP-friendliness of multimedia flows in a way that the refined model provides more accurate throughput estimate of a TCP flow particularly when the number of TCP flows sharing a bottleneck link increases. Through extensive simulations, we show that the proposed scheme indeed improves the quality of the delivered video significantly while maintaining TCP-friendliness in a wireless environment for the case of wireless MPEG-4 video.