QoE-Driven Adaptive Video Streaming: Architectures, Techniques, and Future Research Challenges Toward 6G Networks
Moner Alsader, Alcardo Alex Barakabitze, Is-Haka Mkwawa
Abstract
The paper provides a survey of architectures and techniques for QoE-driven adaptive video streaming services based on two (2) classifications: client-based video streaming, and delivery-based video rate adaptation. The paper presents in-depth review of QoE- driven network softwarization and virtualization approaches using SDN, NFV, and MEC, leveraging AI/ML techniques and cloud/edge computing architectures. Additionally, the paper provides a review of QoE-driven video streaming in various aspects including 6G-based Metaverse for Multi-User Extended Reality (MER), holographic telepresence, personalized media, Internet of Senses (IoS), Industrial Internet of Things (IIoT) and video coding compression standards. Moreover, the paper provide highlights on multimedia streaming in new verticals and next-generation mobile technologies by putting emphasis in 6G and beyond factories, education, social and entertainment, automotive and healthcare. Finally, the paper present concrete challenges and future research directions in emerging applications, video standards and new business cases towards 6G networks. This paper aims to guide and inspire the multimedia and networking research community both in academia and industry toward developing innovative solutions for monitoring, managing, and optimizing performance in future 6G networks.