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.
The field of multimedia information systems encompasses a wide range of disciplines, including animations, artificial reality, augmented reality, and virtual realities. This article explores the potential of using generative AI (GAI) in the context of mobile multimedia networks (MMNs), particularly in delivering low-latency and high-quality entertainment and tactical applications.
One of the key challenges in maintaining large-scale military multimedia applications in MMNs is scalability and sustainability. To address this, data-driven networking solutions are leveraged to optimize streaming strategies based on real-time traffic analysis and resource monitoring. These solutions can be further enhanced by incorporating GAI techniques, which have the ability to augment data and develop AI-generated content (AIGC) and AI-aided perception.
The framework of GAI-enabled MMNs is proposed in this article. This framework harnesses the capabilities of GAI in data and content synthesis to distribute high-quality and immersive interactive content in wireless networks. The three main features of this framework include distribution, generation, and perception.
The distribution aspect focuses on efficiently transmitting multimedia content by leveraging GAI techniques. This ensures that the content reaches users with minimal latency and high quality. The generation feature explores the potential of using GAI to create new content, enhancing the variety and richness of multimedia experiences in MMNs.
Lastly, the perception component incorporates AI-aided perception techniques to enhance the user experience. This can involve personalized content recommendations based on user preferences and context, as well as real-time adaptation of content based on user feedback.
Furthermore, the article proposes a second-score auction mechanism for allocating network resources within the GAI-enabled MMNs. This mechanism takes into account the values of GAI models and other relevant metrics to efficiently allocate resources. The experimental results demonstrate that this auction mechanism effectively increases social welfare by allocating resources and models that result in the highest user satisfaction.
In conclusion, the integration of generative AI in mobile multimedia networks holds great potential for delivering high-quality and immersive multimedia experiences. This multidisciplinary approach combines concepts from multimedia information systems, animations, artificial reality, augmented reality, and virtual realities to create a framework that optimizes content distribution, generation, and perception in wireless networks.