泰克Sentry解决方案帮助有线电视运营商确保其自适应流媒体服务质量

业内最先进的体验质量(QoE)监视器现在可识别自适应流媒体内容中的视频和音频错误广播视频测试、监测及分析解决方案的市场领导厂商——泰克公司日前宣布,其Sentry数字内容监视器现在可使有线电视运营商通过快速识别和诊断可能影响观众体验质量(QoE)的视频和音频质量问题,以确保其使用自适应流媒体技术提供的直播和     

一种基于HTTP长连接的自适应流媒体传输系统

提出了一种基于HTTP的自适应流媒体传输系统,一方面,该系统利用了HTTP长连接技术,有效地减少了频繁建立与断开TCP连接带来的网络资源开销;另一方面,该系统采用了一种服务器端驱动的码率自适应控制策略,这种策略不仅有效地适应于网络环境的变化,还降低了对客户端的要求,具有很强的通用性。实验结果表明该系统方案是有效的。     

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。     

移动流媒体QoE指标体系构建和量化

随着3G技术的逐步成熟,移动流媒体业务已成为移动业务研究的热点。业务质量的优劣直接影响用户的使用感受,因此对于移动流媒体业务性能的评估只注重业务的QoS机制的改善和提高是不够的,更要注重用户体验满意度的提升。文章分析了移动流媒体业务QoS的影响因素,并基于服务等级协议SLA以及TMF提出的QoE性能指标参考,建立了由QoS关键性能指标到QoE映射的架构,同时提出了一种可量化移动流媒体业务QoE的方法,为服务商提升用户体验质量提供参考。     

Aspera-fasp技术在视频CDN系统中的应用与研究

【】本文研究、设计了一个基于HTTP Live Streaming视频流媒体技术的视频直播和点播的CDN系统,并在此基础上对其进行优化。目前,传统的CDN系统虽适用于目前国内大部分的网络环境,但在某些极端恶劣的环境条件下,比如无专线网络情况下的跨国数据传输,还是无法满足正常的业务需求。因此,本设计引入了Aspera-fasp传输技术,这是一项突破性的传输协议,能充分利用现有的WAN基础设施和通用硬件,让其为传统的CDN系统,提供全球数据的快速传输。     

浅谈HTTP Adaptive Streaming技术及其前景

HTTP Adaptive Streaming（以下简称“HAS”）技术结合了传统的流媒体技术和HTTP渐进式下载播放的特点．以HTTP的方式向用户传送媒体内容．该技术的采用可以大大提升用户的媒体播放体验。同时该技术降低了头端服务器的技术复杂度。基于HTTP的传送方式提升了媒体内容在网络设备中的穿透能力,该技术目前已成为流媒体视频行业发展的趋势。     

新余电视台网络流媒体直播系统设计与实施

介绍了新余电视台网络流媒体直播系统的建设。先阐述了网络流媒体直播系统主要功能及优势、系统设计原则、系统设计方案,然后介绍了流媒体直播系统功能及扩展功能。新余电视台网络流媒体直播系统的启用,延伸了电视节目的覆盖面,进一步扩大了电视节目的影响力。     

以SRT技术构建广播级流媒体直播系统——温州广播电视传媒集团流媒体系统建设规划和思考

本文介绍了温州广播电视传媒集团在新时代形势下基于SRT新一代流媒体技术实现全流化流媒体直播和制作业务流程,阐述了温州广播电视传媒集团流媒体直播系统的总体架构、工作流程及系统的亮点,展望流媒体生产系统的技术发展未来趋势和技术方向.     

基于HLS的流媒体播放系统的设计与实现

HTTP Live Streaming（简称HLS）是苹果公司提出的移动流媒体协议,广泛应用于传统和移动互联网多媒体应用.在对HTTP Live Streaming协议进行研究的基础上,结合HTML5一些新的特性,给出了一种在iOS设备上基于HTTP Live Streaming的流媒体播放系统的部署方案.结合实际部署的效果,探讨了该协议的特点和当前情况下存在的一些不足.     

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。     

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.     

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.     

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.     

Adaptive video streaming testbed design for performance study and assessment of QoE

Hypertext Transfer Protocol adaptive streaming switches between different video qualities, adapting to the network conditions, and avoids stalling streamed frames over high‐oscillation client's throughput improving the users' quality of experience (QoE). Quality of experience has become the most important parameter to lead the service providers to know about the end‐user feedback. Implementing Hypertext Transfer Protocol adaptive streaming applications to find out QoE in real‐life scenarios of vast networks becomes more challenging and complex task regarding to cost, agile, time, and decisions. In this paper, a virtualized network testbed to virtualize various machines to support implementing experiments of adaptive video streaming has been developed. Within the test study, the metrics which demonstrate performance of QoE are investigated, respectively, including initial delay (ie, startup delay at the beginning of playback a video), frequency switches (ie, number of times the quality is changed), accumulative video time (ie, number and length of stalls), CPU usage, and battery energy consumption. Furthermore, the relation between effective parameters of QoS on the aforementioned metrics for different segment length is investigated. Experimental results show that the proposed virtualized system is agile, easy to install and use, and costs less than real testbeds. Moreover, the subjective and objective performance studies of QoE evaluation in the system have proven that the segment lengths of 6 to 8 seconds were faired and more efficient than others according to the investigated parameters.     

Design and Implementation of a Network‐Adaptive Mechanism for HTTP Video Streaming

This paper proposes a network‐adaptive mechanism for HTTP‐based video streaming over wireless/mobile networks. To provide adaptive video streaming over wireless/mobile networks, the proposed mechanism consists of a throughput estimation scheme in the time‐variant wireless network environment and a video rate selection algorithm used to increase the streaming quality. The adaptive video streaming system with proposed modules is implemented using an open source multimedia framework and is validated over emulated wireless/mobile networks. The emulator helps to model and emulate network conditions based on data collected from actual experiments. The experiment results show that the proposed mechanism provides higher video quality than the existing system provides and a rate of video streaming almost void of freezing.     

Impact of playout buffer dynamics on the QoE of wireless adaptive HTTP progressive video

The quality of experience (QoE) of video streaming is degraded by playback interruptions, which can be mitigated by the playout buffers of end users. To analyze the impact of playout buffer dynamics on the QoE of wireless adaptive hypertext transfer protocol (HTTP) progressive video, we model the playout buffer as a G/D/1 queue with an arbitrary packet arrival rate and deterministic service time. Because all video packets within a block must be available in the playout buffer before that block is decoded, playback interruption can occur even when the playout buffer is non-empty. We analyze the queue length evolution of the playout buffer using diffusion approximation. Closed-form expressions for user-perceived video quality are derived in terms of the buffering delay, playback duration, and interruption probability for an infinite buffer size, the packet loss probability and re-buffering probability for a finite buffer size. Simulation results verify our theoretical analysis and reveal that the impact of playout buffer dynamics on QoE is content dependent, which can contribute to the design of QoE-driven wireless adaptive HTTP progressive video management.     

Quality of experience evaluation of HTTP video streaming based on user interactive behaviors

User interactive behaviors play a dual role during the hypertext transfer protocol (HTTP) video service: reflection and influence. However, they are seldom taken into account in practices. To this end, this paper puts forward the user interactive behaviors, as subjective factors of quality of experience (QoE) from viewer level, to structure a comprehensive multilayer evaluation model based on classic network quality of service (QoS) and application QoS. First, dual roles of user behaviors are studied and the characteristics are extracted where the user experience is correlated with user interactive behaviors. Furthermore, we categorize QoE factors into three dimensions and build the metric system. Then we perform the subjective tests and investigate the relationships among network path quality, user behaviors, and QoE. Ultimately, we employ the back propagation neural network (BPNN) to validate our analysis and model. Through the simulation experiment of mathematical and BPNN, the dual effects of user interaction behaviors on the reflection and influence of QoE in the video stream are analyzed, and the QoE metric system and evaluation model are established.     

基于OpenFlow的SVC流媒体时延自适应分级传输方法

针对当前互联网流媒体传输的时延敏感性问题,提出一种基于OpenFlow的SVC（scalable video coding,可分级视频编码）流媒体时延自适应分级传输方法,该方法有效结合SVC流媒体可分级和OpenFlow灵活可编程的特性,在网络带宽受限和链路拥塞的复杂网络环境下,通过构建基础层和增强层2个独立路由,实现了动态网络下SVC流媒体分级自适应高效传输。仿真结果表明,该方法在提升SVC流媒体传输效率和质量,改善用户体验方面有重要作用。     

移动网络中HTTP视频流终端数据分析及应用

伴随移动通信技术的发展,越来越多的用户选择通过移动网络随时随地观看网络视频,移动视频业务迅速增长.在现有的无线视频传输方式中,由于TCP的可靠性以及易穿透防火墙和网络地址转化等优点,基于TCP的HTTP流逐渐成为网络视频的主流传榆方式.另一方面,用户对业务体验需求的上升,也给移动数据的传输带来巨大挑战.该文通过采集和分析HTTP流的终端数据,获到了HTTP流在HSPA+中传输特性及关键参数的特征,分析结果表明其视频块传输速率变化与自回归模型近似,并在此基础上,提出一种视频等级自适应控制算法,能够有效地改善用户体验.