Scalable Video Multicast In Next Generation Wireless Communication Networks

Recently, mobile communication networks are developing very fast. Next generation mobile communication networks (LTE, LTE-advanced, and WiMAX/802.16) are featured with high data rate and improved coverage, which will enable real-time video multicast and broadcast services with the requirements of short delay and low error rate, including live video streaming, video conference, online games, and IPTV.Scalable video coding (SVC) and adaptive modulation and coding (AMC) are two key techniques for video multicast in mobile communication networks. It is important to choose appropriate MCS for each video layer and to determine the right resource allocation among multiple video sessions. Therefore, the key issue for this scalable video multicasting problem is how to allocate the radio resources to multiple multicast video sessions and to multiple SVC layers within a session, and how to assign MCS for each allocated video layer, in order to achieve the best video service for all the users in the multicast system. The dissertation focuses on the research of scalable video multicast mechanisms and consists of the following three aspects:(1) Research on scalable video multicast with AMC. First the basic model for scalable multicasting problem is built and the problem is proved to be NP-hard. A pseudo-polynomial basic algorithm that finds the optimal total system utility is then proposed to solve this problem. Simulation results show that the proposed basic algorithm performs better than a greedy algorithm and a heuristic algorithm. Then, the basic model is extended and another pseudo-polynomial algorithm that also finds the optimal total system utility but runs faster than the basic algorithm is proposed. Simulation results show that this algorithm performs the same as the basic algorithm but with lower running time. Finally, the basic model is extended with support of configurable full session coverage requirement. In this extended model, the base station can configure which video session should be transmitted with support of full session coverage. A pseudo-polynomial algorithm is proposed to solve the new problem. Simulation results show that the algorithm can achieve the best system performance with support of full session coverage for some video sessions.(2) Research on scalable video multicast with joint layer resource allocation. In order to improve the bandwidth utilization ratio, the concept of joint layer resource allocation is brought into the basic model, which allows the base station to jointly allocate resources to multiple video layers that are assigned the same MCS. This new problem is formulated and proved to be NP-hard. Then a pseudo-polynomial algorithm that finds the optimal total system utility is developed. To reduce the complexity of the algorithm, Fully Polynomial Time Approximation Scheme (FPTAS) for this problem is also proposed. Simulation results show that the optimal algorithm offers significant improvement on system utility over the basic optimal algorithm which does not support joint layer resource allocation. The proposed approximation algorithm provides controllable tradeoff between performance and computational complexity and it can achieve good performance and low running time with appropriately chosen parameters.(3) Research on reliable scalable video multicast with hybrid FEC/ARQ. In order to improve the reliability for the video multicast, hybrid FEC/ARQ error correction technique is employed in the scalable video multicast mechanism. To control the amount of the feedback packets from the users in the multicast group, two schemes is developed:single representation user feedback and group representation user feedback. In the single representation user feedback scheme, only one user in the multicast group is selected to send the feedback packets. Both an optimal algorithm and a fast greedy algorithm are proposed to solve this problem. In the group representation user feedback scheme, users are grouped into several sub-groups based on their channel conditions. Each sub-group selects a representation user to return the feedback packets. A fast greedy algorithm is proposed to solve this problem.