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

arXiv: https://arxiv.org/abs/2111.03776 · HTML: https://mlsanity.com/paper/2111.03776/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2021.

## Built on by (most cited first, 12 of 12)

- [Resource Allocation of Federated Learning for the Metaverse with Mobile Augmented Reality](https://mlsanity.com/paper/2211.08705/) (2022)
- [AI and 6G into the Metaverse: Fundamentals, Challenges and Future Research Trends](https://mlsanity.com/paper/2208.10921/) (2022)
- [Resource Allocation for Mobile Metaverse with the Internet of Vehicles over 6G Wireless Communications: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2209.13425/) (2022)
- [Multi-attribute Auction-based Resource Allocation for Twins Migration in Vehicular Metaverses: A GPT-based DRL Approach](https://mlsanity.com/paper/2406.05418/) (2024)
- [Tiny Multi-Agent DRL for Twins Migration in UAV Metaverses: A Multi-Leader Multi-Follower Stackelberg Game Approach](https://mlsanity.com/paper/2401.09680/) (2024)
- [Semantic Information Marketing in The Metaverse: A Learning-Based Contract Theory Framework](https://mlsanity.com/paper/2302.11457/) (2023)
- [Human-Centric Resource Allocation for the Metaverse With Multiaccess Edge Computing](https://mlsanity.com/paper/2312.15313/) (2023)
- [Service Reservation and Pricing for Green Metaverses: A Stackelberg Game Approach](https://mlsanity.com/paper/2308.04914/) (2023)
- [Need of 6G for the Metaverse Realization](https://mlsanity.com/paper/2301.03386/) (2022)
- [Dynamic Resource Allocation for Metaverse Applications with Deep Reinforcement Learning](https://mlsanity.com/paper/2302.13445/) (2023)
- [MetaTrading: An Immersion-Aware Model Trading Framework for Vehicular Metaverse Services](https://mlsanity.com/paper/2410.19665/) (2024)
- [Hybrid Stackelberg Game and Diffusion-based Auction for Two-tier Agentic AI Task Offloading in Internet of Agents](https://mlsanity.com/paper/2511.22076/) (2025)

## Abstract (arXiv)

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.