Content area
Abstract
Video transmission over error-prone cognitive radio wireless networks is a challenging task. In much of the traditional tight admission control algorithms for video cognitive users (CUs) in cognitive networks, CUs are admitted sequentially based on the strict quality of service and interference constraints imposed on the cognitive and primary users respectively. The sequential admittance of CUs may impose some form of the queuing delay for time-sensitive CUs which may be unacceptable. On the other hand, traditional admission control schemes do not consider the quality of experience of video users for admitting newly incoming ones. For addressing these issues and obtaining a more flexible quality-centric admission control policy by which the admission system can admit eligible cognitive users in parallel, and to cope with uncertainties in the acceptable levels of the video quality for different CUs (which may use different softwares/hardwares with different capabilities) and interference levels imposed on the primary users, a soft admission control (SAC) technique (named FQAC) is proposed by which the admission probability level for the parallel CUs can intelligently be controlled based on some linguistic input variables. Numerical analysis has been performed to validate the efficiency of the proposed quality-centric SAC mechanism in sparse and dense networking scenarios.