Multi-stream partitioning and parity rate allocation of scalable video content for efficient IPTV delivery

We address the joint problem of clustering heterogenous clients and allocating scalable video source rate and FEC redundancy in IPTV systems. We propose a streaming solution that delivers varying portions of the scalably encoded content to different client subsets, together with suitably selected parity data. We formulate an optimization problem where the receivers are clustered depending on the quality of their connection so that the average video quality in the IPTV system is maximized. Then we propose a novel algorithm for determining optimally the client clusters, the source and parity rate allocation to each cluster, and the set of serving rates at which the source+parity data is delivered to the clients. We implement our system through a novel design based on scalable video coding that allows for much more efficient network utilization relative to the case of source versioning. Through simulations we demonstrate that the proposed solution substantially outperforms baseline IPTV schemes that multicast the same source and FEC streams to the whole client population, as is commonly done in practice today.