Optimization for the Metaverse over Mobile Edge Computing with Play to Earn
CoRR(2023)
摘要
The concept of the Metaverse has garnered growing interest from both academic
and industry circles. The decentralization of both the integrity and security
of digital items has spurred the popularity of play-to-earn (P2E) games, where
players are entitled to earn and own digital assets which they may trade for
physical-world currencies. However, these computationally-intensive games are
hardly playable on resource-limited mobile devices and the computational tasks
have to be offloaded to an edge server. Through mobile edge computing (MEC),
users can upload data to the Metaverse Service Provider (MSP) edge servers for
computing. Nevertheless, there is a trade-off between user-perceived in-game
latency and user visual experience. The downlink transmission of
lower-resolution videos lowers user-perceived latency while lowering the visual
fidelity and consequently, earnings of users. In this paper, we design a method
to enhance the Metaverse-based mobile augmented reality (MAR) in-game user
experience. Specifically, we formulate and solve a multi-objective optimization
problem. Given the inherent NP-hardness of the problem, we present a
low-complexity algorithm to address it, mitigating the trade-off between delay
and earnings. The experiment results show that our method can effectively
balance the user-perceived latency and profitability, thus improving the
performance of Metaverse-based MAR systems.
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