NeTS-ANET: One Video Multicast to Serve Diverse Wireless Receivers

由于用户希望在笔记本电脑和手持设备上观看电视直播和体育赛事，无线视频多播正日益流行。然而问题是无线用户有非常不同的信道特性。将视频单独传输给每个用户是不可扩展的，而传输一个视频流来服务所有用户是一个具有挑战性的问题，尽管他们有不同的频道。这个项目介绍了数字雨，一个新的范例，视频流到异构无线接收器。数字雨建立在压缩感知理论的基础上，开发了一种新的视频多播方法，其中源只是广播属于视频流的编码数据包。接收器利用所有接收到的视频数据包，即使是那些有比特错误的。接收端收集的数据包越多，或者每个数据包中的误码越少，就能看到更高的视频质量，而无需接收端反馈、重传或改变编解码器的速率。本研究在压缩感知理论与实际无线网络设计之间架起了一座桥梁。此外，它显著提高了无线视频多播的可扩展性，因此对新兴的无线视频应用（例如IPTV或基于互联网协议的电视）以及相应的无线行业具有直接和广泛的影响。除了标准的研究生和本科生研究和课程开发外，这项研究还与一项教育计划相结合，并具有协同作用，该计划直接支持麻省理工学院为女性和代表性不足的少数民族提供更多培育环境的努力。最后，与之前的资助工作一样，该项目将产生开放源代码、可下载的代码。

Wireless video multicast is increasingly popular, driven by users' desires to watch live TV and sporting events on laptops and handheld devices. The problem however is that wireless users have very different channel characteristics. Transmitting the video separately to each user is unscalable, yet transmitting a single video stream that serves all users despite their diverse channels is a challenging problem.This project introduces Digital Rain, a new paradigm for streaming video to heterogeneous wireless receivers. Digital Rain builds on the theory of compressed sensing to develop a novel approach to video multicast where the source simply broadcasts coded packets belonging to a video stream. Receivers exploit all received video packets, even those with bit errors. Receivers that collect more packets, or experience fewer bit errors in each packet, see higher video quality without any need to receiver feedback, retransmissions, or changing the rate of the codec.This research creates a bridge between the theory of compressed sensing and practical wireless network design. Further, it significantly improves the scalability of wireless video multicast, and hence has a direct and broad impact on emerging wireless video applications (e.g., IPTV, or Television over Internet Protocol), as well as the corresponding wireless industry. The research is integrated and synergistic with an educational plan that, in addition to standard graduate and undergraduate research and course development, directly supports MIT's effort to provide a more nurturing environment for women and underrepresented minorities. Finally, as in previous funded work, the project will result in open source, downloadable code.