Video traffic engineering for IPTV services

视频流量预计将在未来的多媒体数据中占据相当大的一部分。这激发了对基于IP网络的广播电视传输（IPTV）的研究。因此，视频压缩技术用于将未压缩的原始视频流（~ 25mbps）减少到压缩流（MPEG-2的~4Mbps和MPEG-4编解码器套件的小于2Mbps），以实现当前电信基础设施上多个流的多路复用。直观地说，这一努力需要两个研究团体之间的强大协同作用：视频压缩和编码社区和网络社区。本研究旨在通过建立在当前文献的基础上并提出(i)新的视频跟踪格式和（ii）用于大规模传输视频IP数据包的新的速率控制协议来增加两者之间的对话。在IP网络上传输视频流量所面临的主要挑战是其高比特率可变性和对抖动的敏感性。与语音和数据VBR模型不同，迄今为止，视频流量的统计行为特征主要基于经验分析和/或数学模型，假设传输网络中没有数据包丢失。难点在于视频比特率的变化取决于(1)媒体内容（例如，运动量）和(2)所使用的压缩技术。传输网络服务质量（QoS）参数（丢包或带宽限制）与解码视频质量（终端用户感知的称为“体验质量”（QoE））之间缺乏适当的映射也导致了复杂性。

Video traffic is expected to occupy a significant portion of multimedia data in the future. This motivates the research in the delivery of broadcast TV over IP networks (IPTV). Video compression techniques are thus used to reduce the uncompressed raw video streams (~25 Mbps) to compressed streams (~4Mbps for MPEG-2 and less than 2Mbps for MPEG-4 codecs suite) to enable the multiplexing of several streams over current telecommunication infrastructure. Intuitively, this endeavour requires a strong synergy between the two research communities: the video compression and coding community and the networking community. This research aims to increase the dialogue between the two by building on the current literature and proposing (i) new video trace formats and (ii) new rate-control protocols for mass-delivery of video IP packets. The key challenge posed by the transmission of video traffic over IP networks is its high bit-rate variability and sensitivity to jitter. Unlike voice and data VBR models, characterizing the statistical behaviour of video traffic has been mostly based so far on empirical analysis and/or mathematical modeling that assume no packet losses in the transport network. The difficulty lies in the fact that video bit-rate variation depends on both (1) media content (e.g., amount of motion) and (2) compression technique used. Complications also stem from the lack of adequate mapping between transport network Quality of Service (QoS) parameters (packet-losses or bandwidth constraints) and decoded video quality as perceived by the end-user termed "Quality of Experience" (QoE).