This paper introduces a Network Coding (NC) Assisted Reliable Multi-Source Multicasting (NCRMM) technique for multi-hop wireless mesh networks. The NCRMM scheme decreases the average number of packet transmissions in a network, which in turn reduces resource expenditure and traffic congestion problems. Multicast Least Cost Anypath Routing (MLCAR) algorithm is considered in NCRMM to select the neighbouring nodes participating in efficient packet transmission from multiple source nodes to the corresponding destination sets. At each intermediate node, a Coding Window (CW) is defined which stores packets received at the node from the same and/or different sessions. In the proposed NCRMM algorithm, packets are judiciously combined using intra-session and inter-session NC to ensure efficient distribution of coded packets among the neighbouring nodes. This is done while ensuring that packets transmitted at each source node are reliably received at all nodes of the corresponding destination set. The results are obtained for NCRMM and compared with that of the respective MLCAR and NC-MLCAR approach through extensive simulations. It is observed that the cost of multicasting packets in the NCRMM scheme is significantly less than that of the existing MLCAR and NC-MLCAR schemes for both independent and correlated link scenarios. Results comparing the complexity of the proposed NCRMM scheme with other multicasting schemes are also presented.

本文介绍了一种用于多跳无线网状网络的网络编码（NC）辅助可靠多源多播（NCRMM）技术。NCRMM方案减少了网络中数据包传输的平均数量，从而减少了资源消耗和流量拥塞问题。在NCRMM中考虑了多播最低成本任意路径路由（MLCAR）算法，以选择参与从多个源节点到相应目标集的有效数据包传输的相邻节点。在每个中间节点处，定义了一个编码窗口（CW），该窗口存储从相同和/或不同会话在该节点处接收到的数据包。在提出的NCRMM算法中，使用会话内和会话间NC明智地组合数据包，以确保编码数据包在相邻节点之间的有效分配。这样做是在确保在每个源节点上传输的数据包在相应目标集的所有节点上都可靠地接收到的同时进行的。获得了NCRMM的结果，并通过广泛的仿真将其与相应的MLCAR和NC-MLCAR方法的结果进行了比较。可以看出，在独立和相关链路场景下，NCRMM方案中多播数据包的成本都大大低于现有的MLCAR和NC-MLCAR方案。还提出了将建议的NCRMM方案与其他多播方案的复杂度进行比较的结果。获得了NCRMM的结果，并通过广泛的仿真将其与相应的MLCAR和NC-MLCAR方法的结果进行了比较。可以看出，在独立和相关链路场景下，NCRMM方案中多播数据包的成本都大大低于现有的MLCAR和NC-MLCAR方案。还提出了将建议的NCRMM方案与其他多播方案的复杂度进行比较的结果。获得了NCRMM的结果，并通过广泛的仿真将其与相应的MLCAR和NC-MLCAR方法的结果进行了比较。可以看出，在独立和相关链路场景下，NCRMM方案中多播数据包的成本都大大低于现有的MLCAR和NC-MLCAR方案。还提出了将建议的NCRMM方案与其他多播方案的复杂度进行比较的结果。