摘要
随着计算机技术、视频编码压缩技标准和通信技术的不断进步,流媒体服务系统得到了飞速的发展和广泛的应用。如何高效和高质量的提供流媒体服务,也成为当前热门的研究问题。
实际中流媒体服务系统仍然面临着诸多问题。首先,流媒体服务的QoE (Quality of Experience,体验质量)缺乏准确的描述方法,导致从体验质量角度来优化流媒体服务的研究也难以开展。网络QoS (Quality of Service,服务质量)参数通常被传统网络业务用来描述服务质量,但并不适用于流媒体服务。其次,由于IP网络存在延时、丢包、误码、带宽波动等问题,并且仅提供尽力而为的服务,而流媒体服务对实时性和平滑性有着较高要求,两者的矛盾也是亟待解决的问题。本文围绕流媒体服务的QoE,从QoE的描述方法、不同传输协议下流媒体服务流化控制方法几个方面进行了研究。本文主要的工作和创新点如下：
1.提出一种可度量的QoE描述方法和基于QoE优化的流媒体服务体系结构
为了能从QoE的角度进行流媒体服务的优化,提出一个可度量的QoE描述方法SQoE (Scalable QoE),将QoE和网络QoS参数建立映射关系。该描述方法以主流编码标准中的解码依赖关系为依据,结合常见的错误隐藏技术,定量的分析了丢包、延时和抖动等网络QoS参数对QoE的影响,为从QoE角度进行流媒体服务系统的优化研究提供了量化的参考方法。在此描述方法的基础上,提出一种基于QoE优化的流媒体服务体系框架,利用反馈控制的思想,在传统的流媒体服务系统结构的基础上增加反馈回路,通过采集、分析、决策和执行四个模块,将QoE和流媒体服务的质量控制联系起来。所提出的QoE描述方法不需要事先训练样本,通用性更好,实验结果表明,与现有方法相比,准确率最低可提高5%。
2.提出一种基于QoE优化的HTTP自适应流化控制方法QASH
针对目前HTTP自适应流化方法存在的质量抖动和带宽利用率不高的缺陷,从QoE的角度出发,提出了一种基于QoE的HTTP自适应流化控制方法。该方法综合考虑网络状况和客户端缓存状况,重点解决自适应流化控制中带宽估计和码流选择这两个问题：首先是快速和较准确的评估当前网络状况,确定可选的流媒体质量范围；其次在考虑QoE的基础上,根据客户端缓存状态,选择合适质量的流媒体数据提供服务,尽可能提高QoE。通过实验与微软公司的HTTP自适应流化系统进行对比,带宽利用率最低可提高11%。
3.提出一种基于QoE优化的RTP/RTCP自适应流化控制方法
RTP/RTCP流化协议传输本身不具备流化控制机制,为了实现TCP友好性,通常采用TFRC等方法来进行流化控制。通过分析TFRC吞吐量模型,指出其在网络状态波动的情况下吞吐量过低以及对网络变化响应太慢的缺陷,提出一种基于QoE优化的自适应流化控制方法QTFRC (QoE-aware TCP-Friendly Rate Control)。针对无线网络场景,进一步提出了一种基于分类和统计的丢包区分方法提高QTFRC的准确性。实验结果表明,QTFRC方法在网络波动的情况下可以保证TCP友好性,具有更好的实时响应性,同时保持了TFRC方法平滑的优点；在QoE上也有明显改善,视频质量最大可提升2dB。
上述研究成果已经应用到国家863“分布式宽带业务制作协同环境的开发”,国家863“互动电视综合服务系统”等项目中。
Along with the rapid development of computer technology, video compression technology and communication technology, the streaming media services have been fest developed and widely deployed. The research on efficient and highly reliable streaming media services is quite popular now.
There are still a lot of difficulties for streaming media services. First, it's still an open issue to measure the quality of streaming media services and it's difficult to optimize the services from the point of service quality. The network QoS (Quality of Service) parameters are usually used by traditional network services to describe thenir service quality. However, they are not totally suitable for streaming media service. Second, sensitivity for real-time and smoothness makes streaming media services unsuitable to transmitted via IP networks for the inherent characters of IP netwoks such as delay, transmission error, packet loss, bandwidth fluctuation and the best-effort service it offored. In this paper, we focus on the description for QoE and streaming control strategies under different transmission protocols. The main content is as follows:
1. In order to optimize the streaming media service from the point of QoE, we propose a scalable QoE description method by combine QoE and network QoE parameters together. The description provides quantitative analysis of QoE by consideration of inherent characters of state-of-art media codec compression algorithms and error concealment technologies. Then we propose a streaming media service framework based on the description method by adding a feedback loop to traditional streaming media servive framework with the idea of feedback control. The feedback loop consists of four major components as sensors, analytical module, decision module and controllers. Compared to other methods, the proposed description method doesn't need to train sample data and increases description accuracy by5%at least.
2. To tackle the quality fluctuation and low bandwidth utilization of adaptive streaming service based on HTTP nowadays, we propose a QoE-based adaptive streaming control method that joints both network situation and end-user buffer length. The method focuses on two major issues:the quick and accurate estimation of present network bandwidth and the choice of optimize segments to provide optimize QoE for customer. Compared to Microsoft and Adobe adaptive streaming service over HTTP, the proposed method increase bandwidth utilization by11%at least and improve service smoothness.
3. Streaming protocol RTP/RTCP lacks of streaming control strategy and usually the TFRC method is used to provide streaming control. We pointout the shortcomings of the classical TFRC throughput model for fow throughput under the circumstance of network bandwidth fluctuation and slow adaption to the rapid change of network. We propose an QoE-aware top-friendly rate control algorithm (QTFRC) based on TCP throughput model. Furthermore we propose a lost packet classifying algorithm to improve accuracy of QTFRC in the wireless network. These two optimizations make QTFRC can achieve better adaption performance in network fluctuation situations while maintain tcp-friendly and smoothness and improve QoE by increase video quality up to2dB.
Part of research results mentioned above has been successfully applied into projects as follows:863project Collaborative Environment for Distributed Broadband Service Production in Shanghai and Interative TV Integrated Service System in Jiading, Shanghai
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