Intelligent Multicast Internet Radio

Abstract

As the internet continues to mature static content will continue to give way to dynamic, interactive multimedia content. This content will enrich all online media while at the same time imposing heavy bandwidth usage on the underlying network infrastructure. Multicast networking will undoubtedly increase in popularity to relieve the congested networks. Even as the scaling power of multicast networking approaches, users are burdened by the amount of multimedia and have to spend countless hours searching for the appropriate content. Recommender systems are a key way to personalize this content. This thesis is aimed at developing a scalable streaming application over the current IP multicast infrastructure and then using personalization for recommending and customizing dynamic multimedia content. The system uses a web based personalization system as the basis for user interaction and data collection. The multimedia streams are delivered through either Multicast or Unicast depending on each user's capabilities. In short it is a completely scalable, intelligent, audio streaming application.