This paper explores the integrative application of generative artificial intelligence (AI) and digital twin technologies within the forthcoming framework of 6G networks. By outlining current technological advancements, potential applications, and their broad impacts on communication technologies, the article highlights how this integration could drive unprecedented efficiencies and innovations in network management, service delivery, and user experiences. Generative AI, known for its capability to generate and simulate complex data and scenarios, alongside digital twins that provide real-time simulation and analytics of both physical and virtual systems, are poised to support the future development of communications technology. In a 6G context, the combination of these technologies is expected to enhance network design, enable predictive maintenance, and foster personalized user experiences through ultra-high-speed, low-latency, and massive data handling capabilities.

With the evolution of 6G networks, the convergence of the Metaverse and video streaming has opened new avenues for immersive user experiences. However, delivering high-quality video streaming in such an interactive and virtual environment poses significant challenges, particularly with regards to Quality of Experience (QoE). This paper investigates the impact of user interaction behaviors on video streaming performance in the Metaverse, specifically over 6G networks. Through a comprehensive analysis of the factors affecting QoE-such as latency, bandwidth, and interactive response times-this study proposes a novel QoE optimization framework. The framework integrates user interaction data with network resource allocation strategies, leveraging the capabilities of 6G, including ultralow latency and high-speed communication. Experimental results demonstrate improved QoE for Metaverse video streaming when user interaction behaviors are taken into account, providing insights for future developments in immersive media over 6G.

In the rapidly evolving field of multimedia services, video streaming has become increasingly prevalent, demanding innovative solutions to enhance user experience and system efficiency. This paper introduces a novel approach that integrates user digital twins-a dynamic digital representation of a user's preferences and behaviors-with traditional video streaming systems. We explore the potential of this integration to dynamically adjust video preferences and optimize transcoding processes according to real-time data. The methodology leverages advanced machine learning algorithms to continuously update the user's digital twin, which in turn informs the transcoding service to adapt video parameters for optimal quality and minimal buffering. Experimental results show that our approach not only improves the personalization of content delivery but also significantly enhances the overall efficiency of video streaming services by reducing bandwidth usage and improving video playback quality. The implications of such advancements suggest a shift towards more adaptive, user-centric multimedia services, potentially transforming how video content is consumed and delivered.

This paper presents an innovative framework for enhancing vehicular multimedia communication through adaptive computing offloading and dynamic radio access control, targeting the unique challenges of vehicular environments such as high mobility and variable network conditions. Our approach optimizes multimedia content delivery by intelligently managing computing resources and network bandwidth, focusing on maintaining high quality of service (QoS). The system uses advanced algorithms to make real-time decisions for offloading computational tasks and prioritizing multimedia traffic, ensuring efficient resource utilization and improved communication reliability. We validate our framework with simulations that demonstrate significant performance benefits over existing methods, offering potential enhancements in traffic safety and passenger multimedia experience.

With the growing demand for high-definition video streaming services, optimizing network performance is increasingly important. Network Digital Twins (NDTs) offer real-time virtual replicas of physical networks, enabling simulation, prediction, and optimization of network behaviors. This paper proposes a framework that integrates NDTs with buffer control and video caching strategies to enhance video streaming performance. The framework dynamically adjusts buffer sizes and caching policies based on network predictions from the NDT. Simulation results demonstrate that our approach reduces average latency by 25% and buffer underruns by 40% compared to traditional methods. The study highlights the potential of NDTs in improving network management and video streaming services.