A Survey on QoE Management Schemes for HTTP Adaptive Video Streaming: Challenges, Solutions, and Opportunities

With the emergence of new video streaming technologies, advancements in networking paradigms, and the increasing popularity of mobile and smart devices, we are witnessing phenomenal growth in live video traffic over the Internet. To effectively address the explosive growth of multimedia applications over the Internet, it is crucial to consider scalability, quality, and security. The reliability of HTTP Adaptive Streaming (HAS), which leverages TCP, encourages many Over-the-Top (OTT) providers to adopt progressive streaming technology. Monitoring network traffic patterns and client behaviors provides client-side players with greater intelligence to adapt suitable video quality. However, the inflexibility and inefficiency of legacy networks and streaming applications often diminish the perceived streaming quality for clients. This study aims to explore the interplay between machine learning, emerging network architectures, and streaming technology paradigms. Furthermore, it survey the technical challenges within the adaptive video streaming and content delivery technologies, where adaptive streaming leverages advancements in network and artificial intelligence paradigms, edge computing, and NFV-SDN technologies to better adapt to network dynamics and enhance Quality of Experience (QoE). This study focuses exclusively on papers published in the last five years.