  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.
