用图神经网络实现6G流媒体按需组播，兼顾低延迟与个性化画质。
Graph Neural Network-Based Multicast Routing for On-Demand Streaming Services in 6G Networks
- 基于GNN和强化学习构建可动态调整的组播树，复用路径降低开销。
- 在大规模动态网络中接近最优解，计算复杂度显著低于传统方法。
- 适合高带宽6G应用，如全息视频、多感官元宇宙场景。
第六代（6G）无线网络中，实时体感流媒体与多感官扩展现实等高带宽应用激增，亟需支持差异化服务质量的智能组播路由方案。传统最短路径与组播算法或计算开销过大，或结构僵化，难以满足异构用户需求，导致资源利用效率低下。基于神经网络的方法虽推理速度快，但缺乏拓扑泛化能力与可扩展性。本文提出一种基于图神经网络（GNN）的组播路由框架，联合最小化总传输成本并满足用户特定的视频质量要求。将路由问题建模为约束最小流优化任务，设计强化学习算法通过复用路径并适应网络动态，逐步构建高效组播树。采用图注意力网络（GAT）作为编码器提取上下文感知节点嵌入，长短期记忆（LSTM）模块建模路由决策的序列依赖关系。大量仿真表明，该方法在逼近基于动态规划的最优解的同时，显著降低计算复杂度；结果还验证了其在大规模、动态网络拓扑上的强泛化能力，凸显其在6G多媒体传输场景中的实时部署潜力。代码已开源：https://github.com/UNIC-Lab/GNN-Routing。
原文摘要 · Abstract (English)
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.
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