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Multi-objective optimization for non-panoramic VR in mobile edge computing
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DOI：10.23919/JCC.fa.2023-0607.202511.png)
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Abstract
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Non-panoramic virtual reality (VR) provides users with immersive experiences involving strong interactivity, thus attracting growing research and development attention. However, the demand for high bandwidth and low latency in VR services presents greater challenges to existing networks. Inspired by mobile edge computing (MEC), VR users can offload rendering tasks to other devices. The main challenge of task offloading is to minimize latency and energy consumption. Yet, in non-panoramic VR scenarios, it is essential to consider the Quality of Perceptual Experience (QOPE) for users. Simultaneously, one must also take into account the diverse requirements of users in real-world scenarios. Therefore, this paper proposes a QOPE model to measure the visual quality of non-panoramic VR users and models the non-panoramic VR task offloading problem based on MEC as a constrained multi-objective optimization problem (CMOP) that minimizes latency and energy consumption while providing a satisfied QOPE. And we propose an evolutionary algorithm (EA), G-NSGA-II, to solve the CMOP. Simulation results show that the algorithm can effectively find various trade-off solutions among the objectives, satisfying the requirements of different users.
Keywords:
mobile edge computing
multi-objective optimization
non-panoramic VR
task offloading
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