一种基于罚因子的DASH调度算法

随着移动互联网的普及,基于DASH的流媒体传输协议的应用越来越广泛。如何在带宽波动较大的移动互联网环境中保证用户实现流媒体的流畅点播,提高用户的体验度是DASH调度算法最主要研究的问题。以提高用户体验度为出发点,结合带宽和缓存深度两方面因素,对带宽预测模型的置信度进行评价。在高置信度情况下,大胆地对网络带宽估计模型的模型参量进行调整;在低置信度情况下,以保护缓冲区深度为目的,谨慎地对模型参量进行调整。这种调整势必会对QoE造成相应的影响,该影响作为"罚因子"反馈回模型置信度的评价,以获得模型参数的动态最优解,得到一种较好的DASH调度算法。     

Energy efficient fuzzy-based DASH adaptation algorithm

With the expansive demand for video streaming over mobile networks, it is necessary to adopt schemes that balance the need for high video quality with the available network resources when streaming or downloading the video. Several approaches were proposed in the literature, including Dynamic Adaptive Streaming over HTTP (DASH). In this work, we consider an approach in which we place sufficient emphasis on the constrained battery resources in mobile devices when making decisions on the quality (or bitrate) of the video to be requested. This is done by using a fuzzy logic controller that enhances the performance of the Fuzzy-based DASH (FDASH) scheme. Simulation results show that our proposed approach conserves more energy than its predecessor while maintaining similar video quality and avoiding playback interruptions.     

Design of Synchronization and T‐STD Model for 3DTV Service over Hybrid Networks

The objective of digital broadcasting has evolved from providing a plain video service to offering a realistic visual experience. Technologies such as 3DTV and UHDTV have been suggested to achieve this new objective by providing an immersive and stereoscopic visual experience. However, owing to the high bandwidth requirements of such services, the broadcasting industry has faced a challenge to find a new transport mechanism for overcoming the bandwidth limitation. The standardization organizations, the Advanced Television Systems Committee, Digital Video Broadcasting, and Telecommunications Technology Association, have been working on the integration of broadcasting and a broadband network (IP) to resolve the bandwidth issue of realistic video services. This paper introduces a frame‐level timeline synchronization and transport system target decoder model for providing a stable 3DTV service over a hybrid network. The experimental results indicate that the proposed technologies can be successfully adopted as a reference model in a broadcast‐broadband hybrid 3DTV service and other IP‐associated hybrid broadcasting services.     

D2D-assisted user-centric adaptive video transmission in next generation cellular networks

Next-generation cellular networks need to provide seamless connectivity with higher data rates, increased capacity, and enhanced network coverage. As multimedia service demands in various heterogeneous devices grow rapidly compared to the underlying network’s capacity and bandwidth, the adaptation in multimedia streaming services is essential for providing satisfactory Quality of Experience (QoE). This paper develops a Device-to-Device (D2D)-assisted Utility-based Adaptive Multimedia (video) Streaming scheme (UAMS) using D2D communication in a 5th Generation (5G) cellular network where low-battery users may extend their streaming duration by spending lower reception energy with the help of D2D-assisted communication. The adaptation algorithm considers four utility functions: quality, power consumption, packet error ratio, and remaining battery of the user devices to adapt the bitrate dynamically and augment viewers’ experience. We formulate an optimization problem to maximize the joint utility function to provide the best adaptive multimedia content selected for transmission to the end-users either directly or via D2D Relay Nodes (DRNs) in every scheduling interval. We use a graph theoretic approach for choosing the best DRNs. Extensive simulations show the efficacy of the proposed scheme in terms of saved battery energy, churn rate, and QoE metrics compared to a few well-known existing schemes in the literature that do not use D2D communication.     

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.     

基于对比敏感度的DASH客户端码率选择算法研究

基于带宽估算的码率选择算法具有带宽利用率高的优点,但是同时也存在容易受网络波动影响造成计算出的目标码率出现瞬时峰值而造成带宽浪费的问题。针对于此,该文提出一种基于人眼对比敏感度特征的码率选择算法,在客户端利用人眼对比敏感度模型计算当前观看条件下人眼截止空间频率,选择服务器中和截止空间频率差的绝对值最小视频分片对应的码率作为目标码率。和基于带宽估算选择目标码率的方法进行对比实验,测试二者在不同视角内计算的目标码率,得到两者的码率计算结果阶梯图。实验结果表明,在视角为5到15情况下,所提方法较带宽估计方法在确保视频效果前提下能够有效节约带宽。     

Improving Perceived Quality of Live Adaptative Video Streaming

Quality of service (QoS) requirements for live streaming are most required for video-on-demand (VoD), where they are more sensitive to variations in delay, jitter, and packet loss. Dynamic Adaptive Streaming over HTTP (DASH) is the most popular technology for live streaming and VoD, where it has been massively deployed on the Internet. DASH is an over-the-top application using unmanaged networks to distribute content with the best possible quality. Widely, it uses large reception buffers in order to keep a seamless playback for VoD applications. However, the use of large buffers in live streaming services is not allowed because of the induced delay. Hence, network congestion caused by insufficient queues could decrease the user-perceived video quality. Active Queue Management (AQM) arises as an alternative to control the congestion in a router’s queue, pressing the TCP traffic sources to reduce their transmission rate when it detects incipient congestion. As a consequence, the DASH client tends to decrease the quality of the streamed video. In this article, we evaluate the performance of recent AQM strategies for real-time adaptive video streaming and propose a new AQM algorithm using Long Short-Term Memory (LSTM) neural networks to improve the user-perceived video quality. The LSTM forecast the trend of queue delay to allow earlier packet discard in order to avoid the network congestion. The results show that the proposed method outperforms the competing AQM algorithms, mainly in scenarios where there are congested networks.