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

TL;DR

This paper introduces an optimal-cost Steiner tree approach for 6G multimedia delivery, jointly optimizing flow and QoS to minimize network costs and improve efficiency in heterogeneous, on-demand streaming scenarios.

Contribution

It proposes the first dynamic programming-based OST algorithm that jointly optimizes flow aggregation and QoS-aware routing for 6G multimedia distribution.

Findings

OST reduces total network flow by over 10% compared to existing methods.

The algorithm guarantees minimum-cost multicast flow under differentiated QoS constraints.

Extensive experiments validate OST's effectiveness in 6G-like multimedia scenarios.

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.