# Generative AI-Enabled Energy-Efficient Mobile Augmented Reality in Multi-Access Edge Computing
## 1 Introduction  
Mobile augmented reality (MAR), which seamlessly combines virtual information into a person's real-world environment, is becoming a prominent application within mobile multimedia networks (MMNs) by utilizing artificial intelligence (AI) [1]. For example, various deep learning (DL)-based AI object detection algorithms, such as YOLO, SSD, and Fast R-CNN, are employed to identify objects from images in MAR. While these DL algorithms provide precise object detection, they demand significant computational power from MAR devices that have limited processing capabilities and battery life. This leads to increased processing delays and quick battery depletion. To address this issue, multi-access edge computing (MEC) in MMNs provides additional computing resources to mobile devices (MDs) through computation offloading [2]. Since both MDs and MEC servers have heterogeneous computing and networking capabilities, designing efficient offloading management to optimize energy efficiency, minimize latency, or balance both is challenging [2]. Particularly, determining whether to offload images based on current networking and computing conditions has been extensively studied [3, 4, 5]. However, a significant issue in MAR is that frequently transmitting high-resolution images to the MEC server results in high latency and energy consumption. To address this, recent works [6; 7; 8] have considered resolution management to balance accuracy and latency/energy trade-offs, recognizing that image resolution impacts the object detection accuracy. Thus, maintaining an acceptable detection accuracy in MAR while reducing the bitrate remains a challenging trade-off. To address this, recent research in MMNs has explored the potential of generative AI (GAI), such as super-resolution (SR) neural models, to enhance image quality by increasing the resolution of lower-quality frames, thereby reducing the bitrate of network transmission [1] (GAI can produce high-quality, naturalistic content in various formats, including images, videos, and 3D content [1; 9]. Among the various GAI solutions, SR technologies such as EDSR and RCAN convert low-resolution images into high-resolution ones using machine learning (ML)-based AI schemes.). However, GAI technology demands substantial computational power and can introduce latency, which may significantly burden the MEC server if there are numerous offloading requests from MAR clients for MAR services [10]. To the best of our knowledge, no studies have investigated the use of GAI technology for MEC-assisted MAR. Therefore, a thorough investigation is necessary to determine how to efficiently utilize GAI while managing offloading decisions, presenting an opportunity to improve upon conventional approaches. In this study, we propose a novel offloading and SR control scheme for energy-efficient MAR in MEC by leveraging GAI. Specifically, we adopt SR as a promising GAI technology and conduct an empirical study to verify that the computational latency of SR, including SRGAN and EDSR, increases with the upscaling level. From this empirical study, we highlight a trade-off between increased computational latency and enhanced service satisfaction when upscaling images for object detection, as it boosts the detection accuracy. This makes it difficult to decide whether to choose SR for MAR while simultaneously managing offloading decisions. As a result, to balance the trade-off between energy efficiency and latency in MAR with MEC, our approach considers both the computational latency for SR at MEC servers and the computing and networking conditions of MDs to determine whether to offload the image to the MEC server. If offloading is decided, SR management is conducted. To achieve this, we rigorously formulate analytical models and design an elaborate cost optimization problem that balances this trade-off by controlling the offloading decisions and SR management of each MAR client.