Generative AI-enabled Mobile Tactical Multimedia Networks: Distribution, Generation, and Perception

TL;DR

This paper proposes a framework for mobile multimedia networks enhanced with generative AI, focusing on content distribution, generation, and perception, and introduces a resource allocation auction mechanism to improve user satisfaction.

Contribution

It introduces a novel GAI-enabled MMN framework with three key features and a second-score auction mechanism for resource allocation considering GAI model values.

Findings

The auction mechanism effectively increases social welfare.

GAI enhances content quality and network efficiency.

Framework supports immersive and interactive multimedia applications.

Abstract

Mobile multimedia networks (MMNs) demonstrate great potential in delivering low-latency and high-quality entertainment and tactical applications, such as short-video sharing, online conferencing, and battlefield surveillance. For instance, in tactical surveillance of battlefields, scalability and sustainability are indispensable for maintaining large-scale military multimedia applications in MMNs. Therefore, many data-driven networking solutions are leveraged to optimize streaming strategies based on real-time traffic analysis and resource monitoring. In addition, generative AI (GAI) can not only increase the efficiency of existing data-driven solutions through data augmentation but also develop potential capabilities for MMNs, including AI-generated content (AIGC) and AI-aided perception. In this article, we propose the framework of GAI-enabled MMNs that leverage the capabilities of GAI in data and content synthesis to distribute high-quality and immersive interactive content in wireless networks. Specifically, we outline the framework of GAI-enabled MMNs and then introduce its three main features, including distribution, generation, and perception. Furthermore, we propose a second-score auction mechanism for allocating network resources by considering GAI model values and other metrics jointly. The experimental results show that the proposed auction mechanism can effectively increase social welfare by allocating resources and models with the highest user satisfaction.