This article studies the scalable broadcast scheme realized with the joint application of layered source coding,unequal error protection （UEP） and random network coding from the theoretical point of view.The success probability for any non-source node in a heterogeneous network to recover the most important layers of the source data is deduced.This probability proves that in this broadcast scheme every non-source node with enough capacity can always recover the source data partially or entirely as long as the finite field size is sufficiently large.Furthermore,a special construction for the local encoding kernel at the source node is proposed.With this special construction,an increased success probability for partial decoding at any non-source node is achieved,i.e.,the partial decodability offered by the scalable broadcast scheme is improved.
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.
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.
