In this study, a framework is proposed to stream different scalable videos from a base station to multiple secondary users over cognitive radio networks. The objective of this work is to ensure that the secondary users will enjoy continuous video playback with acceptable perceptual quality. To achieve such goal, a channel allocation algorithm is introduced to adaptively assign the available channels to the secondary users while taking into considerations their buffer occupancies. In addition, a streaming algorithm is devised to ensure the delivery of scalable video frames within the delay constraints with priority given to the base layers to guarantee the continuity of video playback. Moreover, the introduced algorithm adapts the employed modulation level based on the channel state information as fed-back by the secondary users. The simulation results show that the proposed streaming algorithms ensure the desirable fairness when allocating the channels between the secondary users. This in turn resulted in efficient usage of the available resources of the cognitive network which is demonstrated in the achieved PSNR of the reconstructed video streams with no interruptions in the playback process. We also showed that scalable video sequences outperform their single-layer counterparts in terms of the achieved video quality.
['Ala Eldin Omer', 'Mohamed Hassan', 'Mohamed El-Tarhuni']
An Adaptive Channel Assignment Approach for Streaming of Scalable Video over Cognitive Radio Networks