Wireless Edge-Empowered Metaverse: A Learning-Based Incentive Mechanism for Virtual Reality

• URL: http://arxiv.org/abs/2111.03776v1
• Date: Sun, 7 Nov 2021 13:02:52 GMT
• Title: Wireless Edge-Empowered Metaverse: A Learning-Based Incentive Mechanism for Virtual Reality
• Authors: Minrui Xu, Dusit Niyato, Jiawen Kang, Zehui Xiong, Chunyan Miao, and Dong In Kim
• Abstract summary: We propose a learning-based Incentive Mechanism framework for VR services in the Metaverse. First, we propose the quality of perception as the metric for VR users in the virtual world. Second, for quick trading of VR services between VR users (i.e., buyers) and VR SPs (i.e., sellers), we design a double Dutch auction mechanism. Third, for auction communication reduction, we design a deep reinforcement learning-based auctioneer to accelerate this auction process.
• Score: 102.4151387131726
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Metaverse is regarded as the next-generation Internet paradigm that allows humans to play, work, and socialize in an alternative virtual world with immersive experience, for instance, via head-mounted display for Virtual Reality (VR) rendering. With the help of ubiquitous wireless connections and powerful edge computing technologies, VR users in wireless edge-empowered Metaverse can immerse in the virtual through the access of VR services offered by different providers. However, VR applications are computation- and communication-intensive. The VR service providers (SPs) have to optimize the VR service delivery efficiently and economically given their limited communication and computation resources. An incentive mechanism can be thus applied as an effective tool for managing VR services between providers and users. Therefore, in this paper, we propose a learning-based Incentive Mechanism framework for VR services in the Metaverse. First, we propose the quality of perception as the metric for VR users immersing in the virtual world. Second, for quick trading of VR services between VR users (i.e., buyers) and VR SPs (i.e., sellers), we design a double Dutch auction mechanism to determine optimal pricing and allocation rules in this market. Third, for auction communication reduction, we design a deep reinforcement learning-based auctioneer to accelerate this auction process. Experimental results demonstrate that the proposed framework can achieve near-optimal social welfare while reducing at least half of the auction information exchange cost than baseline methods.

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