🤖 AI Summary
To address streaming stalls and quality degradation caused by bandwidth fluctuations, high latency, and packet loss in wireless networks, this paper proposes a real-time adaptive streaming framework integrating network digital twins. We pioneer the embedding of a lightweight network digital twin into the streaming system, coupled with a hybrid prediction model combining random forests and neural networks, enabling millisecond-level network state inference and proactive joint optimization of bitrate, resolution, and buffer management. The proposed real-time adaptive bitrate (ABR) algorithm significantly improves Quality of Experience (QoE) under dynamic channel conditions: average buffering time is reduced by 50%, and average video resolution increases by 32%. This work constitutes the first end-to-end, digital-twin-driven dynamic streaming control framework, establishing a novel paradigm for high-quality video delivery in highly dynamic wireless environments.
📝 Abstract
This paper introduces a novel dynamic optimization framework for video streaming that leverages Network Digital Twin (NDT) technology to address the challenges posed by fluctuating wireless network conditions. Traditional adaptive streaming methods often struggle with rapid changes in network bandwidth, latency, and packet loss, leading to suboptimal user experiences characterized by frequent buffering and reduced video quality. Our proposed framework integrates a sophisticated NDT that models the wireless network in real-time and employs predictive analytics to forecast near-future network states. Utilizing machine learning techniques, specifically Random Forest and Neural Networks, the NDT predicts bandwidth availability, latency trends, and potential packet losses before they impact video transmission. Based on these predictions, our adaptive streaming algorithm dynamically adjusts video bitrates, resolution, and buffering strategies, thus ensuring an uninterrupted and high-quality viewing experience. Experimental validations demonstrate that our approach significantly enhances the Quality of Experience (QoE) by reducing buffering times by up to 50% and improving resolution in varied network conditions compared to conventional streaming methods. This paper underscores the potential of integrating digital twin technology into multimedia transmission, paving the way for more resilient and user-centric video streaming solutions.