Joint Source-Channel Rate Allocation and Client Clustering for Scalable Multistream IPTV

We design a system framework for streaming scalable internet protocol television (IPTV) content to heterogenous clients. The backbone bandwidth is optimally allocated between source and parity data layers that are delivered to the client population. The assignment of stream layers to clients is done based on their access link data rate and packet loss characteristics, and is part of the optimization. We design three techniques for jointly computing the optimal number of multicast sessions, their respective source and parity rates, and client membership, either exactly or approximatively, at lower complexity. The latter is achieved via an iterative coordinate descent algorithm that only marginally underperforms relative to the exact analytic solution. Through experiments, we study the advantages of our framework over common IPTV systems that deliver the same source and parity streams to every client. We observe substantial gains in video quality in terms of both its average value and standard deviation over the client population. In addition, for energy efficiency, we propose to move the parity data generation part to the edge of the backbone network, where each client connects to its IPTV stream. We analytically study the conditions under which such an approach delivers energy savings relative to the conventional case of source and parity data generation at the IPTV streaming server. Finally, we demonstrate that our system enables more consistent streaming performance, when the clients' access link packet loss distribution is varied, relative to the two baseline methods used in our investigation, and maintains the same performance as an ideal system that serves each client independently.