  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.
