A scalable QoS adaptation scheme for media servers

The issue of providing efficient media retrieval services has been an important problem in recent years. Among them, video-on-demand (VoD) is one of the most popular and challenging ones. An efficient solution to the VoD service problem should address the twin issues of scalability and client heterogeneity. In this paper, we propose a VoD server model, that is based on the parallel video server architecture and layered coding, to satisfy these twin issues. The proposed model dynamically adapts to the server load in order to maximize the number of clients (connections) admitted while maintaining a high quality for the already admitted connections. We study the adaptiveness our model by proposing promotion/demotion policies for quality adaptation. We also propose a performance metric, called marginal quality, that normalizes the quality (received) across heterogeneous connections. Finally, we evaluate the effectiveness of our model, in terms of connection acceptance rate, connection quality, and fairness in connection quality, through extensive simulation studies.