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As the next-generation wireless communication system, Sixth-Generation (6G)technologies are emerging, enabling various mobile edge networks that canrevolutionize wireless communication and connectivity. By integratingGenerative Artificial Intelligence (GAI) with mobile edge networks, generativemobile edge networks possess immense potential to enhance the intelligence andefficiency of wireless communication networks. In this article, we propose theconcept of generative mobile edge networks and overview widely adopted GAItechnologies and their applications in mobile edge networks. We then discussthe potential challenges faced by generative mobile edge networks inresource-constrained scenarios. To address these challenges, we develop auniversal resource-efficient generative incentive mechanism framework, in whichwe design resource-efficient methods for network overhead reduction, formulateappropriate incentive mechanisms for the resource allocation problem, andutilize Generative Diffusion Models (GDMs) to find the optimal incentivemechanism solutions. Furthermore, we conduct a case study onresource-constrained mobile edge networks, employing model partition forefficient AI task offloading and proposing a GDM-based Stackelberg model tomotivate edge devices to contribute computing resources for mobile edgeintelligence. Finally, we propose several open directions that could contributeto the future popularity of generative mobile edge networks.

Resource-efficient Generative Mobile Edge Networks in 6G Era: Fundamentals, Framework and Case Study