Network digital twins (NDT) are transformative technologies that replicate physical network infrastructures in a virtual environment. These virtual models integrate real-time data feeds and advanced predictive analytics to simulate and forecast network behaviors, thus facilitating proactive management and decision-making processes. This technology underpins critical improvements in operational efficiency, reliability, and innovation in network management. By offering insights into potential failures and system efficiencies before they manifest in the physical world, NDTs enable stakeholders to make more informed, data-driven decisions. This paper explores the intricate framework of NDTs, their application across diverse industries, and the challenges they face, emphasizing their role in optimizing network performance and driving the next wave of digital transformation.

This paper investigates the transformative role of 6G networks in revolutionizing short video streaming, a sector that has witnessed exponential growth due to platforms such as TikTok, Instagram Reels, and Snapchat. With 6G's potential to deliver data speeds up to 100 times faster than 5G, coupled with near-zero latency and advanced network capabilities like edge computing and network slicing, the streaming of short videos is expected to achieve unprecedented quality and efficiency. We explore how these technological advancements will facilitate ultra-high-definition (UHD) streaming, enhance real-time interactivity, and enable deeper personalization through AI-driven analytics. Additionally, the paper discusses the implications for content creators and consumers, including new opportunities for engagement and the emergence of innovative business models, as well as the broader impacts on global media distribution networks. The integration of these technologies promises not only to refine user experiences but also to challenge existing media distribution paradigms, calling for novel regulatory and security frameworks to navigate the enhanced connectivity and data management capabilities of 6G networks.

The advent of 6G networks promises unprecedented advancements in mobile communication technology, offering ultra-low latency, enhanced reliability, and massive connectivity. Video streaming, a dominant use case, will significantly benefit from these advancements. However, managing buffer control to ensure seamless and high-quality video streaming in 6G remains a significant challenge. This paper explores buffer control mechanisms optimized for 6G environments, considering factors such as high data rates, network variability, and user mobility. We propose a dynamic buffer control algorithm that adapts to varying network conditions, ensuring minimal latency, high-resolution video quality, and uninterrupted playback. Our algorithm comprises key components including real-time network monitoring, predictive buffer management using machine learning models, mobility adaptation, and Quality of Experience (QoE) optimization. Through extensive simulations using a 6G network emulator, we demonstrate that our proposed algorithm significantly outperforms existing Adaptive Bitrate Streaming (ABR) algorithms in terms of reducing buffering events, enhancing video quality, and providing a smoother playback experience. The results underscore the potential of advanced buffer control mechanisms to fully exploit the capabilities of 6G technology, thereby enhancing the video streaming experience for end-users.