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.

翻译：移动多媒体网络在提供低延迟、高质量的娱乐与战术应用方面展现出巨大潜力，例如短视频共享、在线会议以及战场监控。以战术战场监控为例，可扩展性与可持续性对于维护大规模军事多媒体应用而言不可或缺。为此，许多数据驱动的网络解决方案被用于基于实时流量分析和资源监测来优化流媒体策略。此外，生成式人工智能不仅能够通过数据增强提升现有数据驱动方案的效率，还能为移动多媒体网络开发潜在能力，包括人工智能生成内容和人工智能辅助感知。本文提出了生成式AI赋能的移动多媒体网络框架，该框架利用生成式AI在数据与内容合成方面的能力，在无线网络中分发高质量、沉浸式的交互内容。具体而言，我们概述了生成式AI赋能的移动多媒体网络框架，并介绍了其三大主要特性：分发、生成与感知。进一步地，我们提出了一种联合考虑生成式AI模型价值及其他指标的二级评分拍卖机制，用于分配网络资源。实验结果表明，所提出的拍卖机制能够通过将资源与模型分配给用户满意度最高的组合，有效提升社会福利。