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

Minrui Xu, Dusit Niyato, Jiawen Kang, Zehui Xiong, Song Guo, Yuguang Fang, Dong In Kim

TL;DR

The paper addresses the challenge of delivering scalable, low-latency, high-quality multimedia in mobile networks by integrating Generative AI into distribution, generation, and perception functions. It introduces a framework for GAI-enabled MMNs and surveys algorithmic options (GANs, VAEs, diffusion, transformers) for content generation and delivery, along with three key capabilities: distribution, content intelligence, and perception intelligence. A novel second-score auction is proposed to allocate edge resources to multiple GAI models, aiming to maximize social welfare and user satisfaction. Experimental scenarios illustrate that the second-score auction can outperform traditional second-price auctions in revenue and resource utilization, highlighting the practical value for near-term deployment in tactical surveillance and immersive services.

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.

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

TL;DR

The paper addresses the challenge of delivering scalable, low-latency, high-quality multimedia in mobile networks by integrating Generative AI into distribution, generation, and perception functions. It introduces a framework for GAI-enabled MMNs and surveys algorithmic options (GANs, VAEs, diffusion, transformers) for content generation and delivery, along with three key capabilities: distribution, content intelligence, and perception intelligence. A novel second-score auction is proposed to allocate edge resources to multiple GAI models, aiming to maximize social welfare and user satisfaction. Experimental scenarios illustrate that the second-score auction can outperform traditional second-price auctions in revenue and resource utilization, highlighting the practical value for near-term deployment in tactical surveillance and immersive services.

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.
Paper Structure (24 sections, 4 figures, 1 table)

This paper contains 24 sections, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. The Framework of Generative AI-enabled Mobile Multimedia Networks
  3. Generative AI for Multimedia Networks
  4. Multimedia Distribution Intelligence
  5. Dynamic Adaptive Streaming
  6. Buffering and Caching
  7. Energy Efficiency
  8. Multimedia Content Intelligence
  9. Content Synthesis, Repairing and Editing
  10. Image Super-resolution and Video Interpolation
  11. Cross-modal Content Synchronization and Animation
  12. Multimedia Perception Intelligence
  13. Personalized Content Translation and Style-Transfer
  14. Generative AI Agent-Human Multimodal Interaction
  15. Potential Applications and Challenges in Generative AI-enabled MMNs
  16. ...and 9 more sections

Figures (4)

  • Figure 1: The overview of Generative AI-enabled MMNs, including content distribution solutions, dynamic content generation, quality enhancement, and interactive perception based on GAI techniques.
  • Figure 2: Model markets for Generative AI-enabled MMNs with multiple GAI models, e.g., VideoMAE V2 (https://github.com/OpenGVLab/VideoMAEv2), InternVideo (https://github.com/OpenGVLab/InternVideo), UMT (https://github.com/TencentARC/UMT), for various downstream tasks.
  • Figure 3: The workflow of proposed auction-based resource allocation for GAI models in Generative AI-enabled MMNs.
  • Figure 4: The market revenue versus the size of foundational models for mobile multimedia services.