As more and more video content is made available and accessed on-demand, content and service providers face challenges of scale. Today’s delivery mechanisms, especially unicast, require resources to scale linearly with the number of receivers and library sizes. Unlike these mechanisms, with multicast, the load on a server is relatively independent of the number of receivers. Adopting multicast for on-demand access, however, is challenging because of the need to temporally aggregate requests. In this paper, we investigate the importance of an intelligent scheduler and a good data model for achieving good aggregation of requests into multicast groups. We examine the use of an Earliest Deadline First (EDF)-like scheduler that aims to schedule the transmission of “chunks” of video according to their “deadlines” using multicast. We show through analysis that this approach is optimal in terms of the data transmitted by the server. Using trace data from an operational service, we s...
Vaneet Aggarwal, A. Robert Calderbank, Vijay Gopal