Exploring interest correlation for peer-to-peer socialized video sharing

The last five years have witnessed an explosion of networked video sharing, represented by YouTube, as a new killer Internet application. Their sustainable development however is severely hindered by the intrinsic limit of their client/server architecture. A shift to the peer-to-peer paradigm has been widely suggested with success already shown in live video streaming and movie-on-demand. Unfortunately, our latest measurement demonstrates that short video clips exhibit drastically different statistics, which would simply render these existing solutions suboptimal, if not entirely inapplicable. Our long-term measurement over five million YouTube videos, on the other hand, reveals interesting social networks with strong correlation among the videos, thus opening new opportunities to explore. In this article, we present NetTube, a novel peer-to-peer assisted delivering framework that explores the user interest correlation for short video sharing. We address a series of key design issues to realize the system, including a bi-layer overlay, an efficient indexing scheme, a delay-aware scheduling mechanism, and a prefetching strategy leveraging interest correlation. We evaluate NetTube through both simulations and prototype experiments, which show that it greatly reduces the server workload, improves the playback quality and scales well.