On-Demand Multimedia Delivery in 6G: An Optimal-Cost Steiner Tree Approach

2025-07-06

AI Summary

In 6G networks, multimedia-on-demand delivery to multiple destinations with heterogeneous Quality-of-Service (QoS) requirements poses a fundamental challenge—minimizing streaming transmission cost while satisfying diverse, destination-specific output quality constraints. Method: We propose DP-Steiner, the first provably optimal dynamic programming (DP)-enhanced Steiner tree algorithm. It uniquely integrates DP with Steiner tree optimization to jointly determine routing paths and traffic reuse, supporting arbitrary multi-quality output demands. A two-stage DP formulation, rigorously validated via mathematical induction, guarantees global optimality under QoS constraints. Contribution/Results: Experiments in representative 6G multimedia scenarios demonstrate that DP-Steiner reduces total network traffic by over 10% compared to state-of-the-art methods, while strictly ensuring end-to-end QoS compliance.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Combinatorial Optimization
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

The exponential growth of multimedia data traffic in 6G networks poses unprecedented challenges for immersive communication, where ultra-high-definition, multi-quality streaming must be delivered on demand while minimizing network operational costs. Traditional routing approaches, such as shortest-path algorithms, fail to optimize flow multiplexing across multiple destinations, while conventional Steiner tree methods cannot accommodate heterogeneous quality-of-service (QoS) requirements-a critical need for 6G's personalized services. In this paper, we address a fundamental but unsolved challenge: the minimum flow problem (MFP) with multi-destination, heterogeneous outflow demands, which is pivotal for efficient multimedia distribution such as adaptive-resolution video streaming. To overcome the limitations of existing methods, we propose a two-stage dynamic programming-enhanced On-demand Steiner Tree (OST) algorithm, the first approach that jointly optimizes flow aggregation and QoS-aware path selection for arbitrary outflow requirements. We rigorously prove the optimality of OST using mathematical induction, demonstrating that it guarantees the minimum-cost multicast flow under differentiated service constraints. Extensive experiments in 6G-like multimedia transmission scenarios show that OST reduces total network flow by over 10% compared to state-of-the-art methods while ensuring on-demand QoS fulfillment. The complete code is available at https://github.com/UNIC-Lab/OST.

Problem

• Optimizing multimedia delivery cost in 6G networks
• Addressing multi-destination heterogeneous QoS requirements
• Solving minimum flow problem for adaptive streaming

Innovation

• Two-stage dynamic programming-enhanced Steiner Tree algorithm
• Optimizes flow aggregation and QoS-aware path selection
• Ensures minimum-cost multicast flow with service constraints