Accepted paper @ ACM Computing Surveys

Title: Towards AI-Assisted Sustainable Adaptive Video Streaming Systems: Tutorial and Survey

Authors: Reza Farahani, Zoha Azimi, Christian Timmerer, Radu Prodan

Abstract: Improvements in networking technologies and the steadily increasing number of users, as well as the shift from traditional broadcasting to streaming content over the Internet, have made video applications (Video-on-Demand (VoD) and live streaming) 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 increased energy consumption and a larger 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 article 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 of AI-driven approaches for improving the energy efficiency of end-to-end video streaming systems across encoding, delivery, playback, and VQA stages. It further discusses key challenges in AI-assisted streaming, including ethical concerns (privacy, bias, security), deployment barriers (dataset limitations, scalability, inference efficiency), protocol-level latency-computation trade-offs (e.g., QUIC and WebRTC), and the energy implications of Generative AI and semantic streaming.