Contract-Inspired Contest Theory for Controllable Image Generation in Mobile Edge Metaverse

TL;DR

This paper introduces a contract-inspired contest theory framework combined with deep reinforcement learning and generative diffusion models to improve high-quality image generation in resource-limited mobile edge Metaverse environments.

Contribution

It presents a novel integration of contract and contest theory with DRL and GDMs to optimize resource allocation and image quality in mobile edge computing.

Findings

Improved image quality in resource-constrained environments.

Faster convergence and greater stability compared to traditional methods.

Enhanced resource allocation efficiency in mobile edge Metaverse applications.

Abstract

The rapid advancement of immersive technologies has propelled the development of the Metaverse, where the convergence of virtual and physical realities necessitates the generation of high-quality, photorealistic images to enhance user experience. However, generating these images, especially through Generative Diffusion Models (GDMs), in mobile edge computing environments presents significant challenges due to the limited computing resources of edge devices and the dynamic nature of wireless networks. This paper proposes a novel framework that integrates contract-inspired contest theory, Deep Reinforcement Learning (DRL), and GDMs to optimize image generation in these resource-constrained environments. The framework addresses the critical challenges of resource allocation and semantic data transmission quality by incentivizing edge devices to efficiently transmit high-quality semantic data, which is essential for creating realistic and immersive images. The use of contest and contract theory ensures that edge devices are motivated to allocate resources effectively, while DRL dynamically adjusts to network conditions, optimizing the overall image generation process. Experimental results demonstrate that the proposed approach not only improves the quality of generated images but also achieves superior convergence speed and stability compared to traditional methods. This makes the framework particularly effective for optimizing complex resource allocation tasks in mobile edge Metaverse applications, offering enhanced performance and efficiency in creating immersive virtual environments.