-Efficient and scalable live-streaming overlay construction has become a hot topic recently. In order to improve the performance metrics, such as startup delay, source-to-end delay, and playback continuity, most previous studies focused on intra-overlay optimization. Such approaches have drawbacks including low resource utilization, high startup and source-to-end delay, and unreasonable resource assignment in global P2P networks. Anysee is a peer-to-peer live streaming system and adopts an inter-overlay optimization scheme, in which resources can join multiple overlays, so as to (1) improve global resource utilization and distribute traffic to all physical links evenly; (2) assign resources based on their locality and delay; (3) guarantee streaming service quality by using the nearest peers, even when such peers might belong to different overlays; and (4) balance the load among the group members. We compare the performance of our design with existing approaches based on comprehensive trace driven simulations. Results show that AnySee outperforms previous schemes in resource utilization and the QoS of streaming services. AnySee has been implemented as an Internet based live streaming system, and was successfully released in the summer of 2004 in CERNET of China. Over 60,000 users enjoy massive entertainment programs, including TV programs, movies, and academic conferences. Statistics prove that this design is scalable and robust, and we believe that the wide deployment of AnySee will soon benefit many more Internet users.

Abstract. This paper presents the design and deployment of a locality-aware overlay multicast protocol called Anysee. The key idea of Anysee is to use the geometrical information of end hosts to construct the locality-aware overlay data delivery tree such that nearby users in the underlying network can be organized into nearby subtrees. The prototype of Anysee has been widely used in CERNET. Logging traces obtained from broadcasting 2004 Athens Olympic Games over 16 days have shown that the performance of Anysee, such as end-to-end delay and absolute data delivery delay, significantly outperforms that of randomly constructed overlay multicast.

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Parallel video servers provide highly scalable video-on-demand service for a huge number of clients. The conventional stream-scheduling scheme does not use I/O and network bandwidth efficiently. Some other schemes, such as batching and stream merging, can effectively improve server I/O and network bandwidth efficiency. However, the batching scheme results in long start-up latency and high reneging probability. The traditional stream-merging scheme does not work well at high client-request rates due to mass retransmission of the same video data. In this paper, a novel stream-scheduling scheme, called Medusa, is developed for minimizing server bandwidth requirements over a wide range of client-request rates. Furthermore, the start-up latency raised by Medusa scheme is far less than that of the batching scheme.

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