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The Metaverse is regarded as the next-generation Internet paradigm thatallows humans to play, work, and socialize in an alternative virtual world withimmersive experience, for instance, via head-mounted display for VirtualReality (VR) rendering. With the help of ubiquitous wireless connections andpowerful edge computing technologies, VR users in wireless edge-empoweredMetaverse can immerse in the virtual through the access of VR services offeredby different providers. However, VR applications are computation- andcommunication-intensive. The VR service providers (SPs) have to optimize the VRservice delivery efficiently and economically given their limited communicationand computation resources. An incentive mechanism can be thus applied as aneffective tool for managing VR services between providers and users. Therefore,in this paper, we propose a learning-based Incentive Mechanism framework for VRservices in the Metaverse. First, we propose the quality of perception as themetric for VR users immersing in the virtual world. Second, for quick tradingof VR services between VR users (i.e., buyers) and VR SPs (i.e., sellers), wedesign a double Dutch auction mechanism to determine optimal pricing andallocation rules in this market. Third, for auction communication reduction, wedesign a deep reinforcement learning-based auctioneer to accelerate thisauction process. Experimental results demonstrate that the proposed frameworkcan achieve near-optimal social welfare while reducing at least half of theauction information exchange cost than baseline methods.

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