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 an innovative 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 novel bicriteria optimization problem is formulated--\emph{Minimum Interference Steiner Tree (MIST)}, which is the edge-weighted variant of the vertex-weighted secluded Steiner tree problem \cite{chechik2013secluded}. 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 exploits the monotone submodularity property of the interference metric and identifies Pareto optimal solutions for MIST, then converts the problem into the submodular minimization under Steiner tree constraints, and designs a two-stage relaxation algorithm. Simulation results demonstrate and validate the performance of the proposed algorithm.

翻译：组播是软件定义网络（SDN）中至关重要的信息分发技术。在SDN架构下，组播服务能够整合部署于不同节点的网络功能，此即软件定义组播。面向5G及后5G时代的新型泛在无线网络天然支持组播传输。然而，无线信道的广播特性（尤其在密集部署场景中）会导致邻域干扰成为系统性能的主要制约因素，这为无线Mesh网络中的软件定义组播带来了新的挑战。为解决该问题，本文提出一种创新方法，其核心思想在于同时最小化组播树的总长度代价与网络干扰。基于此，本文构建了一个新颖的双目标优化问题——最小干扰斯坦纳树问题，该问题是顶点加权隔离斯坦纳树问题的边加权变体。针对此双目标优化问题，本文未采用启发式策略，而是设计了一种对MIST问题具有性能保证的近似算法。具体而言，该方法利用干扰度量的单调次模性，识别MIST的帕累托最优解，将原问题转化为斯坦纳树约束下的次模最小化问题，并设计了两阶段松弛算法。仿真结果验证了所提算法的性能表现。