Dynamic adaptive mapping of videos in a hierarchical TV-Anytime server network

In this paper we propose a hierarchical server network architecture for the support of a large-scale TV-Anytime system. We present and formulate various policies used for the effective mapping of videos in the runtime of the TV-Anytime system. A predictive assignment policy is presented to determine periodically the mapping of videos to the server network. Furthermore, we present an adaptive reallocation policy to handle short-term variations. Our simulation results demonstrate that these policies yield an efficient system and make the system more adaptive to changes in video popularity and system state. Our simulation results also show that batching and multicasting significantly improves the system throughput during high load periods and makes the system more scalable.