Supporting Interactive Video-on-Demand With Adaptive Multicast Streaming

Abstract

Recent advances in multicast video streaming algorithms have opened up new ways to provision video-on-demand services to potentially millions of users. However, the spectacular efficiency of multicast streaming algorithms can only be realized by restricting or even prohibiting interactive playback control. Experiments reveal that the performance of current state-of-the-art multicast streaming algorithms will degrade significantly even at very low levels of interactivity (e.g., one control per five users). This study tackles this challenge by investigating the fundamental limitations of multicast streaming algorithms in supporting interactive playback control and presents a general solution-static full stream scheduling (SFSS)-which can be applied to many of the existing multicast streaming algorithms to substantially improve their performance when interactive playback control is to be supported. Moreover, to solve the problem of optimizing the algorithm for the often unknown client access patterns (e.g., arrival rates and interactivity rates), we present a novel just-in-time simulation (JTS) scheme to dynamically and automatically tune operating parameters of the SFSS algorithm while the system is online. This JTS scheme not only eliminates the need for a priori knowledge of the often unknown system parameters, but also can adapt to changes in the client access pattern over time. Extensive simulation results show that the proposed adaptive algorithm can reduce the admission and interactive control latencies by as much as 90%