Effective client-driven three-level rate adaptation (TLRA) approach for adaptive HTTP streaming

Multimedia streaming allows consumers to view multimedia content anywhere. However, quality of service is a major concern amid heightened levels of network traffic caused by increasing user demand. Accordingly, media streaming technology is adopting a new paradigm: adaptive HTTP streaming (AHS). AHS is widely used for real-time streaming content delivery in the Internet environment. In streaming, selection of appropriate bitrate is crucial for adapting media rate to network variations and client processing capabilities while ensuring optimal service for the consumer. We evaluate a proposed client-driven three-level optimized rate adaptation algorithm for adaptive HTTP media streaming. In the first stage, the algorithm chooses a suitable starting bitrate close to the available channel capacity. Next, the algorithm monitors the client parameters in real time, precisely detecting network variations and choosing a likely available bit representation for the next download segment. The algorithm controls and minimizes the effects of buffer stalls and overflow resulting from the brief network variations occurring between consecutive segments. The proposed algorithm is implemented in Dynamic Adaptive Streaming over HTTP (DASH) player and its performance compared to that of commercially available Gstreamer-based HTTP Live Streaming (HLS) and DASH players which use conventional segment fetch time–based adaptation and throughput-based adaptation algorithms respectively. This evaluation uses a real-time cloud server client and test bed streaming setup. The resulting analysis shows that the client-driven three-level rate adaptation (TLRA) approach allows adaptive streaming clients to maximize use of end-to-end network capacity, delivering an ideal user experience by precisely predicting network variations and rapidly adapting to available bandwidth, minimizing rebuffering events and bitrate level changes.     

Multimedia streaming gateway with jitter detection

This paper investigates a novel active buffer management scheme, "Jitter Detection" (JD) for gateway-based congestion control to stream multimedia traffics in packet-switched networks. The quality of multimedia presentation can be greatly degraded due to network delay variation or jitter when transported over a packet-switched network. Jitter degrades the timing relationship among packets in a single media stream and between packets from different media streams and, hence, creates multimedia synchronization problems. Moreover, too much jitter will also degrade the performance of the streaming buffer in the client. Packets received by the client will be rendered useless if they have accumulated enough jitter. The proposed active buffer management scheme will improve the quality of service in multimedia networking by detecting and discarding packets that accumulated enough jitter, such as to maintain a high bandwidth for packets within the multimedia stream's jitter tolerance. Simulation results have shown that the proposed scheme can effectively lower the average received packet jitter and increase the goodput of the received packets when compared to random early detection (RED) and DropTail used in gateway-based congestion control. Furthermore, simulation results have also revealed that the proposed scheme can maintain the same TCP friendliness when compared to that of RED and DropTail used for multimedia streams.     

基于实时流协议的流媒体客户端

介绍了实时流和实时流协议，针对基于实时协议的流媒体客户端播放器的特殊要求，提出了一种适用于视频点播（Video On Demand)的流媒体客户端结构模型，研究并设计了相应的流量控制策略、组包算法和缓存管理策略，试验结果表明，该客户端播放器占用系统资源少，具有良好的实时性、容错性和同步性。     

基于HTTP协议与H.264的YouTube媒体播放器的研究

YouTube是全球著名的视频网站,为用户提供高质量的视频等服务.在移动流媒体服务规范的基础上,设计了YouTube媒体播放器客户端的系统架构,根据播放器的功能设计,分为HTTP引擎、音视频缓冲、音视频解码、音视频同步、自适应和UI界面几个模块.在比较分析移动流媒体传输协议的基础上,重点研究了使用HTTP协议优化网络引擎的实现方案.采用了H.264解码器从算法级和代码级对传输数据进行优化,从而实现媒体播放器在YouTube视频网站上播放视频的功能.     

Resource-aware and quality-fair video-streaming using multiple adaptive TCP connections

In this paper, we present a resource-aware and quality-fair video content sharing system. When a video sharing server has insufficient uplink bandwidth and needs to serve multiple video content sharing services via streaming or downloading to other client peers using TCP transport, each service shares the limited uplink bandwidth equitably, due to the fair sharing characteristics inherent in TCP. However this bandwidth fair sharing cannot always guarantee quality fairness among the services, due to the specific requirements for video-streaming services, such as the playout rate and the size of the playout buffer. In our system, the server uses multiple TCP connections adaptively, depending on the anticipated status of each client playout buffer, to guarantee the bandwidth of each video-streaming session. By guaranteeing the quality of each video-streaming session, without the quality loss of other service sessions, the proposed system can successfully achieve service quality fairness. Simulation results show that our proposed algorithm can dramatically enhance the quality of each streaming session and thus provide service quality fairness among simultaneous multiple heterogeneous video-streaming services and content download services.     

An HTTP/2 Push-Based Approach for Low-Latency Live Streaming with Super-Short Segments

Over the last years, streaming of multimedia content has become more prominent than ever. To meet increasing user requirements, the concept of HTTP Adaptive Streaming (HAS) has recently been introduced. In HAS, video content is temporally divided into multiple segments, each encoded at several quality levels. A rate adaptation heuristic selects the quality level for every segment, allowing the client to take into account the observed available bandwidth and the buffer filling level when deciding the most appropriate quality level for every new video segment. Despite the ability of HAS to deal with changing network conditions, a low average quality and a large camera-to-display delay are often observed in live streaming scenarios. In the meantime, the HTTP/2 protocol was standardized in February 2015, providing new features which target a reduction of the page loading time in web browsing. In this paper, we propose a novel push-based approach for HAS, in which HTTP/2’s push feature is used to actively push segments from server to client. Using this approach with video segments with a sub-second duration, referred to as super-short segments, it is possible to reduce the startup time and end-to-end delay in HAS live streaming. Evaluation of the proposed approach, through emulation of a multi-client scenario with highly variable bandwidth and latency, shows that the startup time can be reduced with 31.2% compared to traditional solutions over HTTP/1.1 in mobile, high-latency networks. Furthermore, the end-to-end delay in live streaming scenarios can be reduced with 4 s, while providing the content at similar video quality.     

Delimiting the range of effectiveness of scalable on-demand streaming

Previous analyses of scalable streaming protocols for delivery of stored multimedia have largely focused on how the server bandwidth required for full-file delivery scales as the client request rate increases or as the start-up delay is decreased. This previous work leaves unanswered three questions that can substantively impact the desirability of using these protocols in some application domains, namely:

Are simpler scalable download protocols preferable to scalable streaming protocols in contexts where substantial start-up delays can be tolerated?

If client requests are for (perhaps arbitrary) intervals of the media file rather than the full-file, are there conditions under which streaming is not scalable (i.e., no streaming protocol can achieve sub-linear scaling of required server bandwidth with request rate)?

For systems delivering a large collection of objects with a heavy-tailed distribution of file popularity, can scalable streaming substantially reduce the total server bandwidth requirement, or will this requirement be largely dominated by the required bandwidth for relatively cold objects?

This paper addresses these questions primarily through the development of tight lower bounds on required server bandwidth, under the assumption of Poisson, independent client requests. Implications for other arrival processes are also discussed. Previous work and results presented in this paper suggest that these bounds can be approached by implementable policies. With respect to the first question, the results show that scalable streaming protocols require significantly lower server bandwidth in comparison to download protocols for start-up delays up to a large fraction of the media playback duration. For the second question, we find that in the worst-case interval access model, the minimum required server bandwidth, assuming immediate service to each client, scales as the square root of the request rate. Finally, for the third question, we show that scalable streaming can provide a factor of log K improvement in the total minimum required server bandwidth for immediate service, as the number of objects K is scaled, for systems with fixed minimum object request popularity.     

一种符合3GPP标准的动态带宽适配算法

3GPP的PSS规范定义了移动流媒体动态带宽适配技术的框架,其具体的适配算法一直是研究的热点;提出一套符合3GPP技术标准的流媒体服务器端的无线网络带宽估计和适配算法;带宽估计算法利用探测和缓冲滤波判断是否达到上切标准;适配算法根据剩余媒体时间重新计算编码流速率,并保证在任何信道容量变化模型下都不发生缓冲区下溢;提出的算法提高了接收端的服务质量且快捷、可靠,不增加网络负担;最后,结合3GPP的技术标准TS26.234给出算法的实现方案和仿真结果。     

QoE-driven in-network optimization for Adaptive Video Streaming based on packet sampling measurements

HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for adaptive streaming solutions. In HAS, a video is temporally split into segments which are encoded at different quality rates. The client can then autonomously decide, based on the current buffer filling and network conditions, which quality representation it will download. Each of these players strives to optimize their individual quality, which leads to bandwidth competition, causing quality oscillations and buffer starvations. This article proposes a solution to alleviate these problems by deploying in-network quality optimization agents, which monitor the available throughput using sampling-based measurement techniques and optimize the quality of each client, based on a HAS Quality of Experience (QoE) metric. This in-network optimization is achieved by solving a linear optimization problem both using centralized as well as distributed algorithms. The proposed hybrid QoE-driven approach allows the client to take into account the in-network decisions during the rate adaptation process, while still keeping the ability to react to sudden bandwidth fluctuations in the local network. The proposed approach allows improving existing autonomous quality selection heuristics by at least 30%, while outperforming an in-network approach using purely bitrate-driven optimization by up to 19%.     

Cooperative Media Data Streaming with Scalable Video Coding

Live peer-to-peer (P2P) streaming has become a promising approach for broadcasting non-interactive media content from a server to a large number of interested clients. However, it still faces many challenges such as high churn rate of peer clients, uplink bandwidth constraints of participating peers, and heterogeneity of client throuput capacities. This paper presents a new P2P network called LSONet, a collaborative peer-to-peer streaming framework for scalable layer-encoded bit streams. The contributions are the combination of the advantages of both layered conding and mesh-based packet exchange. With layered coding, it overcomes overlay bandwidth limitatioins and heterogeneity of client capacities. With mesh based overlay streaming, it can better handle peer churns, as compared to tree-based solutions. For achieving these targets, this paper employs a gossip-based data-driven scheme for partnership formation, and proposes two algorithms, optimized transmission policy (OTP) and graceful degradation scheme (GDS), for multi-layers allocation. The proposed system is completely self-organizing, and in a fully distributed fashion. Extensive simulations show that LSONet achieves higher quality of service by peer-assisted streaming and layered video coding. Also, through comparison, results show that the system outperforms some previous schemes in resource utilization and is more robust and resilient for nodes departure, which demonstrate that it is well-suited for quality adaptive live streaming applications.     

A Statistical Admission Control Scheme for Continuous Media Servers Using Caching

In continuous media servers, disk load can be reduced by using buffer cache. In order to utilize the saved disk bandwidth by caching, a continuous media server must employ an admission control scheme to decide whether a new client can be admitted for service without violating the requirements of clients already being serviced. A scheme providing deterministic QoS guarantees in servers using caching has already been proposed. Since, however, deterministic admission control is based on the worst case assumption, it causes the wastage of the system resources. If we can exactly predict the future available disk bandwidth, both high disk utilization and hiccup-free service are achievable. However, as the caching effect is not analytically determined, it is difficult to predict the disk load without substantial computation overhead. In this paper, we propose a statistical admission control scheme for continuous media servers where caching is used to reduce disk load. This scheme improves disk utilization and allows more streams to be serviced while maintaining near-deterministic service. The scheme, called Shortsighted Prediction Admission Control (SPAC), combines exact prediction through on-line simulation and statistical estimation using a probabilistic model of future disk load in order to reduce computation overhead. It thereby exploits the variation in disk load induced by VBR-encoded objects and the decrease in client load by caching. Through trace-driven simulations, it is demonstrated that the scheme provides near-deterministic QoS and keeps disk utilization high.     

大规模客户端视频流带宽调度

视频流服务的迅猛发展, 大规模用户共享带宽链路的场景不断增多. 现存的DASH视频流采用的ABR算法多用于提高单客户端用户的体验质量(quality of experience, QoE), 还有一些算法仅针对数个客户端的情况. 本文提出一种应用于大规模客户端场景的带宽调度算法, 通过聚类算法减小调度规模, 再将带宽分...     

运动感知基于缓存的自适应视频流传输

目的 基于缓存的自适应视频流传输策略无需估测实时带宽,直接通过缓存变化量与码率的映射函数选取符合当前网络状况的最佳质量码流传输。传统基于缓存的自适应视频传输不考虑内容特征,在码率选择上为不同运动级别视频内容均使用相同的码率映射函数,在不稳定的无线网络环境中高运动强度内容的码率急剧降低会严重伤害用户体验质量（QoE）,提出运动感知基于缓存的自适应视频流传输（MA-BBA）算法。方法 MA-BBA算法根据片段运动级别确定码率映射函数,对运动强度高的内容快速切换到较高码率,而对于运动强度较低的内容则使用较为保守的码率,从而使得缓存资源能够位于安全边界之上且较多分配给高级别运动内容,提高不同运动强度内容的平均质量,使整体QoE得到优化。结果 在公开的无线网络带宽数据集上实现本文MA-BBA算法,基于吞吐量的自适应传输算法（TBA）和基于缓存的自适应传输算法（BBA）。MA-BBA在高运动强度内容的平均质量上比TBA和BBA分别提高1.7%和1.2%,且质量波动区间更小。MA-BBA在平均缓存利用率上达到72%,大大高于TBA的45.9%和BBA的45.4%。结论 MA-BBA算法与现有的码率自适应算法TBA和BBA相比,大大提高了缓存资源利用率,提高了对资源要求最苛刻的高级别运动内容的传输质量,减小码率切换幅度频率,优化了视频服务的整体QoE。     

基于缓存补偿的视频码率自适应算法

针对基于超文本传输协议（HTTP）的动态自适应流（DASH）码率自适应算法未能充分利用视频缓存以及平均码率偏低的问题,提出一种DASH标准的基于缓存补偿的码率自适应切换（BASBC）算法。首先,根据最近下载分片的下载速率分析带宽波动程度并得到预估带宽;其次,依据预估带宽和当前码率等级在缓存区设置码率上切阈值和码率下切阈值,并利用动态上切阈值控制码率向上切换,消耗缓存时长,而利用动态下切阈值控制码率向下逐级切换,累积缓存时长,从而在缓存区形成累积-消耗的缓存状态循环。BASBC算法在视频播放平均码率上高于动态自适应的HTTP流码率渐进切换（DASBS）算法,有效提高了带宽利用率;虽然所提算法的平均码率稍低于基于DASH标准的码率平滑切换（RSS）算法,但所提算法的码率切换更为平滑,整体切换稳定性表现更优。实验结果表明,所提算法在动态网络环境中具有高带宽利用、切换平滑且稳定的良好表现,能够有效提高用户的体验质量（QoE）。     

Complexity of cloud-based transcoding platform for scalable and effective video streaming services

Nowadays, a fast network improves the quality of our daily life and we can enjoy a variety of services over the Internet. Different types of media streaming services have been proposed and utilized as the network speed is now sufficiently fast to deliver high-quality live streaming. Usually, different media streaming services deliver streaming data by using different protocols such as the real-time message protocol (RTMP), real-time streaming protocol (RTSP), and Windows media HTTP streaming protocol (WMSP). In this paper, we propose and implement a cloud-based scalable and cost-effective video streaming transcoding service platform to provide the service of changing real-time streaming protocols (RTMP/RTSP) and codecs (H.263/H.264). A transcoder dispatching problem (TDP) over the cloud platform is also defined, which attempts to serve all the transcoding requests by minimizing the cost of virtual machines. Further, a transcoder dispatching algorithm and an online transcoder dispatching algorithm are proposed for the TDP. These algorithms are implemented on the Amazon EC2 platform. Experimental results demonstrate that by renting different levels of virtual machines dynamically and intelligently, we can provide a scalable and cost-effective transcoding service for bridging heterogeneous streaming media.     

缓存门限自适应调整的智能手机节能研究

为了解决LTE无线流媒体终端能量利用效率低的问题,提出一种新的基于动态缓存门限调整的流媒体传输控制算法(DBTA)。该算法根据监测到的网络带宽和流媒体编码率信息,对终端缓存区下限阈值做出自适应的调整,使缓存空间大小更契合当前带宽和流媒体编码率,提高了终端的休眠工作时间比,进而延长了终端的续航时间。仿真结果表明,该算法可有效解决LTE无线流媒体终端能耗过快的问题,最多可降低约22%的能耗。     

基于P2P的流媒体客户端缓存管理机制的研究

针对在基于P2P的点播系统中,由于客户端缓存区没有得到高效的利用而影响流媒体点播系统的服务质量问题,提出了一种新的基于混合P2P的流媒体点播模型P2P_VOD,该模型将客户端缓存分为三个区,并详细阐述了客户端节点缓存区的缓存替换机制,综合考虑了数据块备份量的均衡性和节点VCR操作的命中率,使得节目数据块在各节点间缓存得到全局优化并有效缓解了服务器负载。通过仿真对比实验,验证了该模型在启动延迟和服务器负载方面的优越性。     

A new fast friendly window-based congestion control for real-time streaming media transmission

1 Introduction In the current Internet, not all applications use TCP and they do not follow the same concept of fairly sharing the available bandwidth. The rapid growing of real-time streaming media applications will bring much UDP traffic without integrating TCP compatible congestion control mechanism into Internet. It threats the quality of service (QoS) of real-time applications and the stability of the current Internet. For this reason, it is desirable to define appropriate rate rule…     

基于P2P视频点播技术的流媒体平台设计与开发

随着流媒体应用在Internet上的流行,传统C/S模式的流媒体服务系统已经不能满足流媒体对服务器性能和高带宽的要求,严重阻碍了流媒体业务质量的提高和容量的扩大。本文介绍一种基于P2P网络的流媒体播放技术,它将P2P网络技术和流媒体技术结合起来,充分利用客户计算机的资源,减轻流媒体服务器和网络负载,突破了传统的流媒体播放系统带宽瓶颈,能够保持播放节目流完整而流畅。本文还采用MVC模式和Java语言以面向对象方法设计和开发P2P流媒体网站,利用P2P流媒体技术,实现校园流媒体的视频点播。