On Video Multicast in Cognitive Radio Networks

We investigate the challenging problem of enabling multicast video service in emerging cognitive radio (CR) networks. We propose a cross-layer optimization approach to multicast video in CR networks. Specifically, we model CR video multicast as an optimization problem, while considering important design factors including scalable video coding, video rate control, spectrum sensing, dynamic spectrum access, modulation, scheduling, retransmission, and primary user protection. The objective is to optimize the overall received video quality as well as achieving proportional fairness among multicast users, while keeping the interference to primary users below a prescribed threshold. Although the problem can be solved using advanced optimization techniques, we propose a sequential fixing algorithm and a greedy algorithm with low complexity and proven optimality gap. Our simulations using MPEG-4 fine grained scalability (FGS) demonstrate the efficacy and superior performance of the proposed approach as compared with an alternative equal allocation scheme.