FTBM: A Fault-Tolerant BIER Multicast for MBMS in 5G/B5G Dynamic Edge Networks

The evolution of 5G and Beyond 5G (B5G) networks has intensified the demand for efficient Multimedia Broadcast Multicast Services (MBMS), particularly in dynamic edge environments. The frequent alterations in network topology and multicast group configurations in these environments present substantial scalability challenges for traditional IP MultiCast (IPMC) mechanisms. Bit Index Explicit Replication (BIER) offers a stateless IPMC alternative that mitigates the limitations of traditional IPMC mechanisms. However, it still encounters fault tolerance issues in dynamic edge networks, where link faults occur frequently. This paper propose a Fault-Tolerant BIER Multicast (FTBM) mechanism specifically designed for MBMS in dynamic edge networks. FTBM optimizes BIER multicast paths by employing Multi-Agent Deep Reinforcement Learning (MADRL) to minimize transmission delays while addressing constraints such as random link faults, limited queue capacity, and forwarding restrictions. Extensive simulations demonstrate that FTBM significantly enhances multicast performance under varying traffic loads and dense fault conditions, leading to improved transmission efficiency and network load balancing. This work provides a resilient and scalable solution for next-generation MBMS in dynamic network environments.