{"ID":2856177,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.11109","arxiv_id":"2510.11109","title":"Graph Neural Network-Based Multicast Routing for On-Demand Streaming Services in 6G Networks","abstract":"The increase of bandwidth-intensive applications in sixth-generation (6G) wireless networks, such as real-time volumetric streaming and multi-sensory extended reality, demands intelligent multicast routing solutions capable of delivering differentiated quality-of-service (QoS) at scale. Traditional shortest-path and multicast routing algorithms are either computationally prohibitive or structurally rigid, and they often fail to support heterogeneous user demands, leading to suboptimal resource utilization. Neural network-based approaches, while offering improved inference speed, typically lack topological generalization and scalability. To address these limitations, this paper presents a graph neural network (GNN)-based multicast routing framework that jointly minimizes total transmission cost and supports user-specific video quality requirements. The routing problem is formulated as a constrained minimum-flow optimization task, and a reinforcement learning algorithm is developed to sequentially construct efficient multicast trees by reusing paths and adapting to network dynamics. A graph attention network (GAT) is employed as the encoder to extract context-aware node embeddings, while a long short-term memory (LSTM) module models the sequential dependencies in routing decisions. Extensive simulations demonstrate that the proposed method closely approximates optimal dynamic programming-based solutions while significantly reducing computational complexity. The results also confirm strong generalization to large-scale and dynamic network topologies, highlighting the method's potential for real-time deployment in 6G multimedia delivery scenarios. Code is available at https://github.com/UNIC-Lab/GNN-Routing.","short_abstract":"The increase of bandwidth-intensive applications in sixth-generation (6G) wireless networks, such as real-time volumetric streaming and multi-sensory extended reality, demands intelligent multicast routing solutions capable of delivering differentiated quality-of-service (QoS) at scale. Traditional shortest-path and mu...","url_abs":"https://arxiv.org/abs/2510.11109","url_pdf":"https://arxiv.org/pdf/2510.11109v1","authors":"[\"Xiucheng Wang\",\"Zien Wang\",\"Nan Cheng\",\"Wenchao Xu\",\"Wei Quan\",\"Xuemin Shen\"]","published":"2025-10-13T08:00:45Z","proceeding":"cs.NI","tasks":"[\"cs.NI\",\"cs.LG\"]","methods":"[\"Graph Neural Network\",\"Reinforcement Learning\"]","has_code":false,"code_links":[{"ID":608321,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2856177,"paper_url":"https://arxiv.org/abs/2510.11109","paper_title":"Graph Neural Network-Based Multicast Routing for On-Demand Streaming Services in 6G Networks","repo_url":"https://github.com/UNIC-Lab/GNN-Routing","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}