Research On Multicast Technology For Streaming Media Transmission In Cognitive Wireless Mesh Networks

With the rapid development of wireless communication technology, the scarcity of radio spectrum and underutilization of licensed spectrum is becoming increasingly prominent. Cognitive wireless mesh networks combine cognitive radio technology with wireless mesh networks, equipping themselves the ability of cognition, reconfiguration and self organization, and thus they become the potential core technology in building the new generation broadband mobile networks. However, due to the affects of multipath fading, intersymbol interference, channel interference and other factors, the efficient transmission of streaming media in wireless network is facing severe challenges. Therefore, in this thesis, the multicast technology for streaming media transmission in cognitive wireless mesh networks is investigated.In terms of the high demands on delay and delay jitter of streaming media, by considering the impact of communication power and channel interference on link capacity, a cross-layer optimization model of streaming media multicast routing is presented based on protocol interference model. At the same time, the user satisfaction evaluation standard is established based on multicast tree delay, and a cross-layer optimization algorithm of streaming media multicast routing is proposed to maximize the customer satisfaction. The genetic algorithm is applied for channel allocation and power control. The ant colony algorithm is used for constructing multicast tree. By using nested optimization of genetic algorithm and ant colony algorithm, joint optimization of the channel allocation, power control and multicast tree construction is realized. Mixed coding and integer coding method are designed respectively to accelerate the convergence rate of the algorithm. The effectiveness of algorithms is verified by a series of simulation experiments.Since the multicast transmission rate has a great impact on streaming media’s performance, and the physical interference model can better represent the SINR of receiving node. In order to maximize the multicast transmission rate, a cross layer optimization model of stream media multicast rate based on the physical interference model is proposed. In this model, the physical link layer and network layer are coupled by capacity constraints, so this model has a clear hierarchical structure. By introducing dual decomposition theory, the original problem is decomposed into a sub-problem of physical link layer and a sub-problem of network layer. As a contribution, a cross-layer optimization algorithm of streaming media multicast rate is proposed based on the dual decomposition. The sub-problem of physical link layer is solved by the genetic algorithm. The sub-problem of network layer is solved by the convex optimization method. Two subproblems are coordinated by the dual variables, and the joint optimization of multicast rate and power control are realized eventually. Since the channel interference seriously affects the communication quality, a cross-layer optimization algorithm of streaming media multicast rate which joints channel allocation and power control is proposed to reduce inter-node interference and improve the transmission rate. Finally, validity of the algorithm is verified by extensive simulation results.