This article studies the problem of constructing optimal layered multicast with network coding for heterogeneous networks. Based on the flexibility of layered source coding, a global-favorable optimization scheme is p...This article studies the problem of constructing optimal layered multicast with network coding for heterogeneous networks. Based on the flexibility of layered source coding, a global-favorable optimization scheme is proposed, which maximizes the aggregate throughput of heterogeneous sink nodes for layered multicast with network coding by determining the optimal bit rates of the layers. To solve this global-favorable optimization scheme, especially in the large-scale heterogeneous networks, a new problem-specific genetic algorithm （GA） is further proposed. It not only searches efficiently for the optimal allocation of layer bit rates, but also guarantees the validity of candidate solutions that this new GA-based optimization scheme could obtain layered multicast with network coding, even in the large-scale in the whole evolutionary process. Simulation results demonstrate efficiently the optimal or satisfactorily near-optimal bit rates for heterogeneous networks.展开更多
To maximize the aggregate throughput achieved in heterogeneous networks, this paper investigates inter-session network coding for the distribution of layered source data. We define inter-layer hierarchical random line...To maximize the aggregate throughput achieved in heterogeneous networks, this paper investigates inter-session network coding for the distribution of layered source data. We define inter-layer hierarchical random linear network codes （IHRLNC）, which not only take the flexibility of intersession network coding for layer mixing but also consider the strict priority inherent in the layered source data. Furthermore, we propose the inter-layer hierarchical multicast （IHM）, which performs IHRLNC in the network such that each sink can recover some source layers according to its individu- al capacity. To determine the optimal type of IHRLNC that should be performed on each edge in IHM, we formulate an optimization problem based on 0-1 integer linear programming, and propose a heuristic approach to approximate the optimal solution in polynomial time. Simulation results show that the proposed IHM can achieve throughput gains over the layered muhicast schemes.展开更多
基金supported by the National Natural Science Foundation of China(60832001)the Science and Technology Supporting Project of Hebei Province of China(072135169)the Postgraduate Innovation Fund of BUPT
文摘This article studies the problem of constructing optimal layered multicast with network coding for heterogeneous networks. Based on the flexibility of layered source coding, a global-favorable optimization scheme is proposed, which maximizes the aggregate throughput of heterogeneous sink nodes for layered multicast with network coding by determining the optimal bit rates of the layers. To solve this global-favorable optimization scheme, especially in the large-scale heterogeneous networks, a new problem-specific genetic algorithm （GA） is further proposed. It not only searches efficiently for the optimal allocation of layer bit rates, but also guarantees the validity of candidate solutions that this new GA-based optimization scheme could obtain layered multicast with network coding, even in the large-scale in the whole evolutionary process. Simulation results demonstrate efficiently the optimal or satisfactorily near-optimal bit rates for heterogeneous networks.
基金Supported by the National Natural Science Foundation of China （ No. 60832001 ）.
文摘To maximize the aggregate throughput achieved in heterogeneous networks, this paper investigates inter-session network coding for the distribution of layered source data. We define inter-layer hierarchical random linear network codes （IHRLNC）, which not only take the flexibility of intersession network coding for layer mixing but also consider the strict priority inherent in the layered source data. Furthermore, we propose the inter-layer hierarchical multicast （IHM）, which performs IHRLNC in the network such that each sink can recover some source layers according to its individu- al capacity. To determine the optimal type of IHRLNC that should be performed on each edge in IHM, we formulate an optimization problem based on 0-1 integer linear programming, and propose a heuristic approach to approximate the optimal solution in polynomial time. Simulation results show that the proposed IHM can achieve throughput gains over the layered muhicast schemes.
