Multicasting is a vital information dissemination technique in Software-Defined Networking (SDN). With SDN, a multicast service can incorporate network functions implemented at different nodes, which is referred to as software-defined multicast. Emerging ubiquitous wireless networks for 5G and Beyond (B5G) inherently support multicast. However, the broadcast nature of wireless channels, especially in dense deployments, leads to neighborhood interference as a primary system degradation factor, which introduces a new challenge for software-defined multicast in wireless mesh networks. To tackle this, this paper introduces a novel approach, based on the idea of minimizing both the total length cost of the multicast tree and the interference at the same time. Accordingly, a bicriteria optimization problem is formulated, which is called \emph{Minimum Interference Steiner Tree (MIST)}. To solve the bicriteria problem, instead of resorting to heuristics, this paper employs an innovative approach that is an approximate algorithm for MIST but with guaranteed performance. Specifically, the approach is a two-stage relaxation algorithm by exploiting the monotone submodularity property of the interference metric and identifying Pareto optimal solutions for MIST. Simulation results demonstrate and validate the performance of the proposed algorithm.

翻译：摘要：组播是软件定义网络（SDN）中的一种关键信息分发技术。借助SDN，组播服务可以整合在不同节点上实现的网络功能，这被称为软件定义组播。新兴的面向5G及未来通信（B5G）的泛在无线网络天然支持组播。然而，无线信道的广播特性（尤其是在密集部署场景中）导致邻域干扰成为系统性能的主要退化因素，这为无线网状网络中的软件定义组播带来了新挑战。为解决此问题，本文提出了一种创新方法，其核心思想是同时最小化组播树的总长度代价与干扰。据此，我们构建了一个双目标优化问题，称为“最小干扰斯坦纳树（MIST）”。为求解该双目标问题，本文未采用启发式算法，而是提出了一种具备性能保证的MIST近似算法。具体而言，该算法通过利用干扰度量的单调子模特性并识别MIST的帕累托最优解，设计了一种两阶段松弛方法。仿真结果验证并展示了所提算法的性能。