Video traffic engineering for IPTV services

视频业务预计将在未来占据多媒体数据的很大一部分。这激发了对基于IP网络的广播电视（IPTV）的交付的研究。因此，视频压缩技术用于将未压缩的原始视频流（~25 Mbps）减少为压缩流（对于MPEG-2为~ 4 Mbps，对于MPEG-4编解码器套件为小于2 Mbps），以使得能够在当前的电信基础设施上复用若干流。直观地说，这一努力需要两个研究社区之间的强大协同作用：视频压缩和编码社区和网络社区。这项研究的目的是增加两者之间的对话，建立在现有的文献，并提出（一）新的视频跟踪格式和（二）新的速率控制协议的大规模交付的视频IP数据包。视频流量在IP网络上传输的关键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，到目前为止，表征视频流量的统计行为主要基于经验分析和/或数学建模，假设传输网络中没有数据包丢失。困难在于视频比特率变化取决于（1）媒体内容（例如，运动量）和（2）所使用的压缩技术。复杂性还源于传输网络服务质量（QoS）参数（分组丢失或带宽约束）与终端用户所感知的解码视频质量（称为“体验质量”（QoE））之间缺乏足够的映射。

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).