  As the essential technical support for Metaverse, Mobile Augmented Reality
(MAR) has attracted the attention of many researchers. MAR applications rely on
real-time processing of visual and audio data, and thus those heavy workloads
can quickly drain the battery of a mobile device. To address such problem,
edge-based solutions have appeared for handling some tasks that require more
computing power. However, such strategies introduce a new trade-off: reducing
the network latency and overall energy consumption requires limiting the size
of the data sent to the edge server, which, in turn, results in lower accuracy.
In this paper, we design an edge-based MAR system and propose a mathematical
model to describe it and analyze the trade-off between latency, accuracy,
server resources allocation and energy consumption. Furthermore, an algorithm
named LEAO is proposed to solve this problem. We evaluate the performance of
the LEAO and other related algorithms across various simulation scenarios. The
results demonstrate the superiority of the LEAO algorithm. Finally, our work
provides insight into optimization problem in edge-based MAR system for
Metaverse.
