# Graph Neural Network-Based Multicast Routing for On-Demand Streaming Services in 6G Networks

arXiv: https://arxiv.org/abs/2510.11109 · HTML: https://mlsanity.com/paper/2510.11109/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 45 references that are in the map.
- First posted on arXiv 13 Oct 2025.

## Builds on (most cited first, 17 of 17)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Construction](https://mlsanity.com/paper/2408.08593/) (2024)
- [Graph Networks with Spectral Message Passing](https://mlsanity.com/paper/2101.00079/) (2020)
- [Toward Immersive Communications in 6G](https://mlsanity.com/paper/2303.05283/) (2023)
- [Spatio-Spectral Graph Neural Networks](https://mlsanity.com/paper/2405.19121/) (2024)
- [Knowledge-Driven Deep Learning Paradigms for Wireless Network Optimization in 6G](https://mlsanity.com/paper/2402.01665/) (2024)
- [DRL-M4MR: An Intelligent Multicast Routing Approach Based on DQN Deep Reinforcement Learning in SDN](https://mlsanity.com/paper/2208.00383/) (2022)
- [Scaling Graph-based Deep Learning models to larger networks](https://mlsanity.com/paper/2110.01261/) (2021)
- [FIGRET: Fine-Grained Robustness-Enhanced Traffic Engineering](https://mlsanity.com/paper/2405.04932/) (2024)
- [Fully Distributed Online Training of Graph Neural Networks in Networked Systems](https://mlsanity.com/paper/2412.06105/) (2024)
- [Opportunistic Routing in Wireless Communications via Learnable State-Augmented Policies](https://mlsanity.com/paper/2503.03736/) (2025)
- [DHRL-FNMR: An Intelligent Multicast Routing Approach Based on Deep Hierarchical Reinforcement Learning in SDN](https://mlsanity.com/paper/2305.19077/) (2023)
- [D3-GNN: Dynamic Distributed Dataflow for Streaming Graph Neural Networks](https://mlsanity.com/paper/2409.09079/) (2024)
- [What should 6G be?](https://mlsanity.com/paper/1906.00741/) (2019)
- [Routing in Small Satellite Networks: A GNN-based Learning Approach](https://mlsanity.com/paper/2108.08523/) (2021)
- [Towards Neural Architecture Search for Transfer Learning in 6G Networks](https://mlsanity.com/paper/2406.02333/) (2024)
- [Some Applications envisaged for the new generation of communications networks 6G](https://mlsanity.com/paper/2501.14117/) (2025)

## Abstract (arXiv)

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.