Robust video-on-demand streaming in peer-to-peer environments
Computer Communications
Traffic and quality characterization of the H.264/AVC scalable video coding extension
Advances in Multimedia
An enhanced client-centric approach for efficient video broadcast
Multimedia Tools and Applications
Quality-aware segment transmission scheduling in peer-to-peer streaming systems
MMSys '10 Proceedings of the first annual ACM SIGMM conference on Multimedia systems
A RF-IPS algorithm for peer-to-peer video-on-demand system
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Efficient push-based packet scheduling for Peer-to-Peer live streaming
Cluster Computing
Hi-index | 0.07 |
Peer-to-peer (P2P) video systems provide a cost-effective way for a large number of hosts to collaborate for video sharing. Two features characterize such a system: 1) a video is usually available on many participating hosts, and 2) different hosts typically have different sets of videos, though some may partially overlap. From a client's perspective, it can be served by any host having the video it requests. From a server's perspective, it be used to serve any client requesting the videos it has. Thus, an important question is, which servers should be used to serve which clients in the system? In this paper, we refer to this problem as service scheduling and show that different matches between clients and servers can result in significantly different system performance. Finding a right server for each client is challenging not only because a client can choose only the servers that are within its limited search scope, but also because clients arrive at different times, which are not known a priori. In this paper, we address these challenges with a novel technique called Shaking. While the proposed technique makes it possible for a client to be served by a server that is beyond the client's own search scope, it is able to dynamically adjust the match between the servers and their pending requests as new requests arrive. Our performance study shows that our new technique can dynamically balance the system workload and significantly improve the overall system performance