Scalable multicast feedback

摘要

Abstract: Within a few years, the number of live audio and video programs available on the Internet will increase dramatically. As the INternet grows as an entertainment medium, content providers are eager to access ratings information to know better what people are interested in and be able to personalize the programs. However, whereas it is easy to know how many people have connected to a given Web site, gathering rating about what multicast viewers are watching does not scale, because there could potentially by millions of viewers connected to a multicast group. This paper presents a novel scheme at the application level to gather feedback form multicast receivers in a scalable way. Our model based on the random sampling of receivers, scale well for very large group since we only need partial feedback to estimate the ratings. !15