Continuous-time collaborative prefetching of continuous media

The real-time streaming of bursty continuous media, such as variable-bit rate encoded video, to buffered clients over networks can be made more efficient by collaboratively prefetching parts of the ongoing streams into the client buffers. The existing collaborative prefetching schemes have been developed for discrete time models, where scheduling decisions for all ongoing streams are typically made for one frame period at a time. This leads to inefficiencies as the network bandwidth is not utilized for some duration at the end of the frame period when no video frame "fits" into the remaining transmission capacity in the schedule. To overcome this inefficiency, we conduct in this paper an extensive study of collaborative prefetching in a continuous-time model. In the continuous-time model, video frames are transmitted continuously across frame periods, while making sure that frames are only transmitted if they meet their discrete playout deadlines. We specify a generic framework for continuous-time collaborative prefetching and a wide array of priority functions to be used for making scheduling decisions within the framework. We conduct an algorithm-theoretic study of the resulting continuous-time prefetching algorithms and evaluate their fairness and starvation probability performance through simulations. We find that the continuous-time prefetching algorithms give favorable fairness and starvation probability performance.