An integrated scheme for streaming scalable encoded video-on-demand over CR networks

Abstract

In this paper, we propose an adaptive scheme to deliver scalable VoD streams over a cognitive radio network (CRN) that consists of a base station (BS) and multiple secondary users (SUs). The proposed scheme aims at improving the perceptual quality of the received videos at the SUs end while maintaining a continuous playback. To do so, the scheme opportunistically exploits the idle primary channel(s) as sensed by the BS. Moreover, to reduce the impact of the intermittent availability nature of the CR channels, we propose a channel allocation algorithm that fairly allocates the idle channel(s) among the active SUs while jointly taking into consideration the status of the playback buffers at the SUs as well as the quality of the available channels. Additionally, the proposed scheme schedules the video frames on the allocated slots by exploiting the flexibility of the scalable video encoder as well as the adaption of the used modulation level based on the channel conditions to meet a predetermined target bit error rate (BER). Simulation results demonstrate the efficiency of the proposed scheme in exploiting the scalability feature of the transmitted video sequences to efficiently use the dynamic CR resources while achieving improved peak signal-to-noise ratio of the reconstructed video streams with uninterrupted playback.