Joint Wireless and Optical Resources Allocation for Energy-Efficient Scalable Video Multicasting

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Authors: Zhilong Zhang, Jiajun Liu, Wei Xun, Sihua Wang, Danpu Liu, Tao Luo
Cited by: 0
References: 33

Abstract

The rapid growth in mobile video services has significantly increased network traffic, posing new challenges for efficient utilization of limited network resources. In this paper, we propose an energy-efficient framework for cross-domain resource coordination tailored specifically to scalable video coding (SVC) multicast transmission. Our work jointly considers both optical resources on the wired side and wireless resources on the wireless side, where we introduce advanced techniques such as multiple-input multiple-output (MIMO) and non-orthogonal multiple access (NOMA) to boost spectral efficiency and better accommodate high data-rate demands. By leveraging the flexible wavelength allocation capability and energy-saving advantages of time and wavelength division multiplexed passive optical network (TWDM-PON), we formulate a unified optimization problem based on a utility function that balances user quality of experience (QoE) and overall system power consumption. The optimization problem involves selecting suitable SVC enhancement layers for users, assigning wireless resources, and allocating optical wavelengths. To simplify this complex process, we divide the original problem into two subproblems: user grouping and resource allocation. The subproblem is converted into a convex form using the convex-concave procedure (CCP) and solved iteratively. Our proposed solution effectively coordinates optical and wireless resources, addressing repeated requests for identical video content alongside QoE requirements. Simulation results demonstrate that our method achieves improved resource utilization and a better balance between QoE and power efficiency compared to existing approaches.

Fields of Study & Subjects

Advanced Photonic Communication Systems Image and Video Quality Assessment Optical Wireless Communication Technologies

Publication details

Journal / source
IEEE Internet of Things Journal
Volume
13
Issue
7
Pages
14992–15004
Publication date
6 January 2026