On-Demand Multimedia Delivery in 6G: An Optimal-Cost Steiner Tree Approach
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912468661436416 |
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| author | Wang, Zien Wang, Xiucheng Cheng, Nan Xu, Wenchao Quan, Wei Sun, Ruijin Zhou, Conghao |
| author_facet | Wang, Zien Wang, Xiucheng Cheng, Nan Xu, Wenchao Quan, Wei Sun, Ruijin Zhou, Conghao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_04589 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On-Demand Multimedia Delivery in 6G: An Optimal-Cost Steiner Tree Approach Wang, Zien Wang, Xiucheng Cheng, Nan Xu, Wenchao Quan, Wei Sun, Ruijin Zhou, Conghao Networking and Internet Architecture Systems and Control 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. |
| title | On-Demand Multimedia Delivery in 6G: An Optimal-Cost Steiner Tree Approach |
| topic | Networking and Internet Architecture Systems and Control |
| url | https://arxiv.org/abs/2507.04589 |