# Generative AI-Enabled Energy-Efficient Mobile Augmented Reality in Multi-Access Edge Computing
## 2 Related Works
### Offloading Management for MAR  
Task offloading is one of the key techniques of an MEC system. It allows MAR users to access the MEC server's resources easily. Furthermore, MAR users no longer have to be concerned about the energy consumption of the MD since the MEC server processes MD's tasks. Many of the early research studies on MEC offloading schemes focused on how to utilize specific objectives with a simple scenario such as the computing resource and storage capacity of the MEC server or energy consumption of the MD [5; 14]. For instance, [PERSON] et al. presented a joint optimization task offloading method for both system response time and energy consumption in a single-device to one mobile edge server scenario [14]. They constructed the fundamental optimization on MEC task offloading for the MD side. Unlike the above, [PERSON] et al. proposed a dynamic offloading scheme for an edge computing-assisted Metaverse system to involve a virtual service provider (VSP) [5]. This aims to minimize the latency of the whole Metaverse service process from a VSP to users with the help of UAVs in an edge computing environment to meet the latency requirements of Metaverse users. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Topic** & **Reference Number** & **Proposed** & **Pros** & **Cons** \\ \hline MAR with GAI & [22] & \begin{tabular}{c} An edge-assisted \\ system that provides \\ distortion-tolerant \\ image recognition for \\ mobile AR with \\ imperceptible system \\ latency \\ \end{tabular} & \begin{tabular}{c} Enables \\ distortion-adaptive \\ image recognition to \\ improve the robustness \\ against image \\ distortions \\ \end{tabular} & \begin{tabular}{c} Do not consider the \\ heterogeneity of the \\ mobile device \\ \end{tabular} \\ \hline MAR with SR control & [24] & \begin{tabular}{c} A motion-guided \\ video-to-video \\ translation framework \\ using a diffusion model \\ \end{tabular} & \begin{tabular}{c} Uses motion \\ information to prevent \\ the regeneration of the \\ redundant areas for \\ content consistency \\ \end{tabular} & \begin{tabular}{c} Focuses on the \\ performance of the GAI \\ model, not the overall \\ system \\ \end{tabular} \\ \hline MAR with SR control & [24] & \begin{tabular}{c} GAN-assisted multi-SR \\ model framework \\ \end{tabular} & \begin{tabular}{c} Analyze the impact of \\ SR on object \\ recognition strictly \\ \end{tabular} & \begin{tabular}{c} Topic-intensive and \\ model-specific \\ model-specific \\ \end{tabular} \\ \hline \multirow{4}{*}{[25]} & \begin{tabular}{c} A MAR performance \\ optimization model \\ based on HD \\ restoration \\ \end{tabular} & \
\begin{tabular}{c} Reduce application cost \\ and ensure the high \\ quality of customer \\ perception \\ \end{tabular} &
\begin{tabular}{c} Do not consider the \\ impact of SR energy \\ consumption \\ requirement on service \\ QoS \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._However, in the real MAR environment, multiple MDs connect to one base station or server, so the research works on the MEC offloading scheme for MAR systems need to design complicated and complex models and solutions to address multi-user environments [13; 16; 17; 18]. [PERSON] et al. provided a general model of the system considering the end-to-end computational latency of MEC applications [16]. They presented complex multi-user offloading problems related to resource sharing and load balancing, and then suggested a solution to this problem. [PERSON] et al. proposed a MEC federation system for MAR with a delay optimization solution based on the Markov Decision Process (MDP) and Deep Deterministic Policy Gradient (DDPG) model to reduce the latency of the communication computation, as well as queuing [17]. [PERSON] et al. proposed a distributed edge system orchestration scheme for a web-based MAR service [18]. This paper suggested a management system for network dynamics with respect to user mobility and workload balance to improve service performance such as message efficiency, scheduling latency, etc.