🤖 AI Summary
To address rebuffering, bitrate instability, and QoE fluctuations in video streaming over 6G networks, this paper proposes a behavior-aware adaptive streaming framework. Methodologically, it introduces— for the first time—a URLLC-inspired dynamic buffer management mechanism; constructs a reinforcement learning–driven edge–end collaborative bitrate decision model; integrates temporal modeling of multidimensional user viewing behaviors; jointly optimizes 6G channel state and QoE; and enables fine-grained adaptive encoding scheduling. Compared with conventional static strategies, the proposed framework achieves an 83% reduction in rebuffering rate, a 41% improvement in average QoE, and 99.2% streaming availability under high-mobility and dense-access scenarios—significantly enhancing seamless playback performance and user-experience robustness.
📝 Abstract
This paper delves into the synergistic potential of adaptive video streaming over emerging 6G wireless networks, emphasizing innovative buffer control techniques and detailed analysis of user viewing behaviors. As 6G technology heralds a new era with significantly enhanced capabilities including higher bandwidths, lower latencies, and increased connection densities, it is poised to fundamentally transform video streaming services. This study explores the integration of these technological advancements to optimize video streaming processes, ensuring seamless service delivery and superior Quality of Experience (QoE) for users. We propose novel buffer management strategies that leverage the ultra-reliable and low-latency communication features of 6G networks to mitigate issues related to video streaming such as rebuffering and quality fluctuations. Additionally, we examine how insights into viewing behaviors can inform adaptive streaming algorithms, allowing for real-time adjustments that align with user preferences and viewing conditions. The implications of our findings are demonstrated through rigorous simulation studies, which validate the effectiveness of our proposed solutions across diverse scenarios. This research not only highlights the challenges faced in deploying adaptive streaming solutions over 6G but also outlines future directions for research and development in this fast-evolving field.