Decision tree modeling for video routing in cognitive radio mesh networks

Cognitive radio networks are developed to solve the under utilization problem of available spectrum. Typically, available spectrum is not fully utilized without supporting multimedia applications. In this work we translate video routing in a dynamic cognitive radio network into a decision theory problem. Then terminal analysis backward induction is used to produce our routing scheme that improves the peak signal-to-noise ratio of the received video. In the proposed Video aware Cognitive Routing strategy (VCR), two components are introduced that improve the precision of correct decision making in a highly dynamic environment; First, a sample and posterior distribution are introduced to explain the status of channels and nodes in supporting video frame quality of service. Second, a utility function is introduced to capture the effects of spectrum stability, fluctuation of bandwidth availability and path quality. In comparison to a deterministic routing scheme developed for dynamic environment (OSDRP), our simulation results show that VCR improves the video quality by at least 30% at the receiver.