Scalable video multicast in cognitive radio networks

We investigate the problem of scalable video multicast in emerging cognitive radio (CR) networks. Although considerable advances have been made in CR research, such important problems have not been well studied. Naturally, ´bandwidth hungry´ multimedia applications are excellent candidates for fully capitalizing the potential of CRs. We propose a crosslayer optimization approach to multicast video in CR networks. Specifically, we consider an infrastructure-based CR network collocated with N primary networks and model CR video multicast over the N channels as a mixed integer nonlinear programming (MINLP) problem. The objective is three-fold: to optimize the overall received video quality; to achieve proportional fairness among multicast users; and to keep the interference to primary users below a prescribed threshold. We propose a sequential fixing algorithm and a greedy algorithm to solve the MINLP, while the latter has low complexity and proven optimality gap. Our simulations with MPEG-4 fine grained scalability (FGS) video demonstrate the efficacy and superior performance of the proposed algorithms.