Streaming Scalable Videos over Multi-Hop Cognitive Radio Networks

We investigate the problem of streaming multiple videos over multi-hop cognitive radio (CR) networks. Fine-Granularity-Scalability (FGS) and Medium-Grain-Scalable (MGS) videos are adopted to accommodate the heterogeneity among channel availabilities and dynamic network conditions. We obtain a mixed integer nonlinear programming (MINLP) problem formulation, with objectives to maximize the overall received video quality and to achieve fairness among the video sessions, while bounding the collision rate with primary users under the presence of spectrum sensing errors. We first solve the MINLP problem using a centralized sequential fixing algorithm, and derive upper and lower bounds for the objective value. We then apply dual decomposition to develop a distributed algorithm and prove its optimality and convergence conditions. The proposed algorithms are evaluated with simulations and are shown to be effective in supporting concurrent scalable video sessions in multi-hop CR networks.