Content Driven Proportionate Channel Allocation Scheme for Scalable Video over Cognitive Radio Network

Abstract

In the cognitive radio network (CRN), secondary users (SUs) may have different video applications that require a distinct channel allocation strategy. In this article, we introduce an efficient channel allocation scheme for different scalable video applications, especially for downlink video streaming, based on the content-driven proportionate channel allocation strategy which considers fairness and application requirements simultaneously. The aim of this channel allocation strategy is to improve the overall satisfaction of the SUs especially for rapid motion (RM) type of video users. RM type generally experiences poor network performance due to the highest motion content and in this article, we have addressed this issue. The CRN base station (CRNBS) gather all content information of the SU’s and perform channel allocation efficiently. In the simulation, we demonstrate that the proposed scheme performs better than the conventional throughput-based rate allocation strategy with proportional fairness in terms of RM user satisfaction.