Peer Selection in Peer-to-Peer Streaming Systems

One important task of any peer-to-peer streaming system (p2p-ss) is how to choose which peers should connect to which peers. How well a p2p-ss perform this task greatly influences its performance. This thesis explores how different peer selection algorithms affect the performance of such systems. A framework for doing the comparisons of peer selection algorithms is built on top of the network simulator ns2, making it possible to later extend the simulations with new peer selection algorithms, congestion control algorithms, wireless networks, cross traffic and other. However, ns2 is a low-level simulator, hence limiting the number of peers in the simulations, because CPU-resources are limited. The simulations are limited to single-layered streams. We find that a centralized selection method, which utilizes knowledge of bandwidth capacities and routing in the network, greatly outperforms both simple random selection of peers, and selection of close peers. Even though centralized selection does not scale well, and is therefore only applicable for a limited number of peers, this shows there is much room for improvement over basic strategies

DAVVI: A prototype for the next generation multimedia entertainment platform

In this demo, we present DAVVI, a prototype of the next generation multimedia entertainment platform. It delivers multi-quality video content in a torrent-similar way like known systems from Move Networks, Microsoft and Apple do. However, it also provides a brand new, personalized user experience. Through applied search, personalization and recommendation technologies, end-users can efficiently search and retrieve highlights and combine arbitrary events in a customized manner using drag and drop. The created playlists of video segments are then delivered back to the system to improve future search and recommendation results. Here, we demonstrate this system using a soccer example