VOVO: VCR-Oriented Video-on-Demand in Large-Scale Peer-to-Peer Networks.

Most P2P Video-on-Demand (VOD) schemes focus more on mending service architectures and optimizing overlays but do not carefully consider the user behavior and the benefit of prefetching strategies. As a result, they cannot better support VCR-oriented services in terms of substantive asynchronous clients or free VCR controls for P2P VODs. To address this issue, we propose VOVO, a VCR-oriented VOD for large-scale P2P networks. By mining associations inside each video, the segments requested in VCR interactivities are predicted based on the information collected through gossips. Together with a hybrid caching strategy, a collaborative prefetching scheme is designed to optimize resource distribution among neighboring peers. We evaluate VOVO through extensive experiments. Results show that VOVO is scalable and effective, providing short start-up latencies and good performance in VCR interactivities. [ABSTRACT FROM AUTHOR]