Abstract
Wireless virtual reality (VR) is expected to be one of the most pivotal applications in 5G and beyond, which provides an immersive experience and will greatly renovate the way people communicate. However, the challenges of VR service transmission to provide high quality of experience (QoE) and a huge data rate remain unsolved. In this paper, we formulate an optimization of the mode selection and resource allocation to maximize the QoE of VR users, aiming at the optimal transmission of VR service based on the cloud-edge-end architecture. Moreover, a distributed game theory based deep reinforcement learning (DGTB-DRL) algorithm is proposed to solve the problem, which can achieve a Nash equilibrium (NE) rapidly. The simulation results demonstrate that the proposed method can achieve better performance in terms of training efficiency, QoE utility values.
This work was supported by National Key R&D Program of China (2020YFB1806702).
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This work was supported by National Key R&D Program of China under Grant 2020YFB1806702.
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Luo, J., Liu, B., Gao, H., Su, X. (2022). Distributed Deep Reinforcement Learning Based Mode Selection and Resource Allocation for VR Transmission in Edge Networks. In: Gao, H., Wun, J., Yin, J., Shen, F., Shen, Y., Yu, J. (eds) Communications and Networking. ChinaCom 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 433. Springer, Cham. https://doi.org/10.1007/978-3-030-99200-2_13
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DOI: https://doi.org/10.1007/978-3-030-99200-2_13
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