Distributed Throughput Maximization in P2P VoD Applications
In peer-to-peer (P2P) video-on-demand (VoD) systems, a scalable source coding is a promising solution to provide heterogeneous peers with different video quality. In this paper, we present a systematic study on the throughput maximization problem in P2P VoD applications. We apply network coding to scalable P2P systems to eliminate the delivery redundancy. Since each peer receives distinct packets, a peer with a higher throughput can reconstruct the video at a higher quality. We maximize the throughput in the existing buffer-forwarding P2P VoD systems using a fully distributed algorithm. We demonstrate in the simulations that the proposed distributed algorithm achieves a higher throughput compared to the proportional allocation scheme or the equal allocation scheme. The existing buffer-forwarding architecture has a limitation in total upload capacity. Therefore we propose a hybrid-forwarding P2P VoD architecture to improve the throughput by combining the buffer-forwarding approach with the storage-forwarding approach. The throughput maximization problem in the hybrid-forwarding architecture is also solved using a fully distributed algorithm. We demonstrate that the proposed hybrid-forwarding architecture greatly improves the throughput compared to the existing buffer-forwarding architecture. In addition, by adjusting the priority weight at each peer, we can implement the differentiated throughput among different users within a video session in the buffer-forwarding architecture, and the differentiated throughput among different video sessions in the hybrid-forwarding architecture.
throughput maximization peer-to-peer (p2p) video-on-demand (vod) distributed algorithm hybrid-forwarding architecture convex optimization