Enhancing User Experience and Energy Efficiency in Metaverse Play to Earn Games via Mobile Edge Computing
The burgeoning interest in the Metaverse has captured the attention of both academic and commercial sectors, largely due to the decentralization of the security and authenticity of digital assets. This has notably accelerated the popularity of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">play-to-earn</i> (P2E) games, where participants can earn and own digital assets that are exchangeable for real-world currencies. These games, however, are computationally demanding and often not feasible on devices with limited resources, necessitating computational offloading to edge servers. Through mobile edge computing (MEC), data can be processed on Metaverse Service Provider (MSP) edge servers. A key challenge arises in balancing the latency perceived by users, which can affect gameplay, the visual experience that impacts potential earnings, and the energy consumption of user devices. Lowering the resolution of downlink transmissions can reduce latency but also diminishes visual quality, consequently affecting user earnings. To address these challenges, our research introduces a comprehensive multi-objective optimization framework that incorporates latency, visual quality, and energy consumption for Metaverse-based mobile augmented reality (MAR) games. Given the NP-hardness of this optimization challenge, we offer simple yet effective optimization algorithms to mitigate the trade-offs between delay and financial returns. Our experimental findings demonstrate that the proposed methodology effectively balances perceived latency, visual quality, and energy consumption, thereby enhancing both the user experience and the sustainability of Metaverse-based MAR systems.
Authors
- Chang Liu (ORCID: https://orcid.org/0000-0003-3767-6533)
- Jun Zhao (ORCID: https://orcid.org/0000-0002-3004-7091)
- Terence Jie Chua (ORCID: https://orcid.org/0000-0002-4983-1740)
Institutions
- Nanyang Technological University (SG)
- Shanghai Jiao Tong University (CN)
Publication Details
- Journal
- IEEE Transactions on Vehicular Technology
- Published
- 2025-09-01
- DOI
- https://doi.org/10.1109/tvt.2025.3600190
- Primary Topic
- Technology Adoption and User Behaviour
- Type
- article
- Field-Weighted Citation Impact
- 0.00