Window-based adaptive technique for real-time streaming of scalable video over cognitive radio networks

In this study, an integrated scheme is proposed to stream real-time scalable videos from a base station to multiple secondary users over cognitive radio networks. The objective of the proposed scheme is to maintain continuous video playback with acceptable perceptual quality at the secondary users end. The proposed scheme is a channel allocation algorithm integrated with a rate control and scheduling algorithms. The channel allocation algorithm is introduced to optimally assign the available channels among the secondary users while taking into considerations their buffer occupancies as well as the channel qualities as seen by the secondary users to meet the requirements of the real-time streamed videos. While the rate control algorithm adapts the source rate to meet the streaming requirements, the scheduling algorithm splits the transmitted video information based on its importance to guarantee the continuity of video playback. The simulation results do not only demonstrate the efficient utilisation of available resources of the cognitive network by the proposed scheme but highlights a desirable need-based fairness when allocating the available channels between the secondary users. The simulation results also indicate that scheduling the transmitted video information on a window basis outperforms frame-based streaming.