Improvements in networking technologies and the steadily increasing numbers of users, as well as the shift from traditional broadcasting to streaming content over the Internet, have made video applications (e.g., live and Video-on-Demand (VoD)) predominant sources of traffic. Recent advances in Artificial Intelligence (AI) and its widespread application in various academic and industrial fields have focused on designing and implementing a variety of video compression and content delivery techniques to improve user Quality of Experience (QoE). However, providing high QoE services results in more energy consumption and carbon footprint across the service delivery path, extending from the end user's device through the network and service infrastructure (e.g., cloud providers). Despite the importance of energy efficiency in video streaming, there is a lack of comprehensive surveys covering state-of-the-art AI techniques and their applications throughout the video streaming lifecycle. Existing surveys typically focus on specific parts, such as video encoding, delivery networks, playback, or quality assessment, without providing a holistic view of the entire lifecycle and its impact on energy consumption and QoE. Motivated by this research gap, this survey provides a comprehensive overview of the video streaming lifecycle, content delivery, energy and Video Quality Assessment (VQA) metrics and models, and AI techniques employed in video streaming. In addition, it conducts an in-depth state-of-the-art analysis focused on AI-driven approaches to enhance the energy efficiency of end-to-end aspects of video streaming systems (i.e., encoding, delivery network, playback, and VQA approaches). Finally, it discusses prospective research directions for developing AI-assisted energy-aware video streaming systems.

翻译：网络技术的进步、用户数量的稳步增长以及从传统广播向互联网流媒体内容的转变，使得视频应用（如直播和视频点播）成为流量的主要来源。人工智能的最新进展及其在众多学术和工业领域的广泛应用，已聚焦于设计和实施多种视频压缩与内容分发技术，以提升用户体验质量。然而，提供高体验质量的服务会导致服务交付路径上更多的能源消耗和碳足迹，这一路径从终端用户的设备延伸至网络及服务基础设施（如云服务提供商）。尽管能效在视频流媒体中至关重要，但目前缺乏全面综述涵盖最先进的人工智能技术及其在整个视频流媒体生命周期中的应用。现有综述通常仅关注特定环节，如视频编码、分发网络、播放或质量评估，未能提供整个生命周期的整体视图及其对能耗和体验质量的影响。受此研究空白驱动，本综述全面概述了视频流媒体生命周期、内容分发、能源与视频质量评估的度量标准及模型，以及视频流媒体中采用的人工智能技术。此外，本文进行了深入的现状分析，重点关注利用人工智能驱动的方法提升视频流系统端到端方面（即编码、分发网络、播放和视频质量评估方法）的能效。最后，本文讨论了开发AI辅助的能源感知视频流系统的未来研究方向。