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.

翻译：在快速发展的多媒体服务领域，视频流媒体已日益普及，亟需创新解决方案以提升用户体验与系统效率。本文提出一种新颖方法，将用户数字孪生——一种动态表征用户偏好与行为的数字化模型——与传统视频流媒体系统相融合。我们探讨了这种融合通过实时数据动态调整视频偏好、优化转码流程的潜力。该方法利用先进的机器学习算法持续更新用户数字孪生，进而指导转码服务自适应调整视频参数，以实现最优画质与最小缓冲延迟。实验结果表明，我们的方案不仅提升了内容分发的个性化水平，还通过降低带宽占用与改善视频播放质量，显著提高了视频流媒体服务的整体效率。此类进展意味着多媒体服务正朝着更具适应性、以用户为中心的方向演进，可能从根本上改变视频内容的消费与分发模式。