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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. |
