🤖 AI Summary
To address the insufficient co-optimization of personalized user experience and adaptive bitrate (ABR) transcoding in video streaming, this paper proposes a user digital twin–driven closed-loop optimization framework. Leveraging online machine learning and behavioral sequence modeling, it constructs fine-grained, real-time user digital twins that dynamically capture individual viewing preferences, device capabilities, and network conditions. These digital twins are deeply integrated into both ABR decision-making and content recommendation pipelines, enabling joint optimization of preference-aware adaptation and transcoding strategies. To the best of our knowledge, this is the first work to systematically incorporate user digital twins into the video streaming architecture. Extensive experiments under realistic traffic conditions demonstrate that the proposed method reduces bandwidth consumption by 18.7%, decreases rebuffering rate by 42%, and improves subjective Quality of Experience (QoE) by 31%, significantly outperforming state-of-the-art baselines.
📝 Abstract
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.