A review on machine learning based user-centric multimedia streaming techniques

The multimedia content and streaming are a major means of information exchange in the modern era and there is an increasing demand for such services. This coupled with the advancement of future wireless networks B5G/6G and the proliferation of intelligent handheld mobile devices, has facilitated the availability of multimedia content to heterogeneous mobile users. Apart from the conventional video, the 360 degrees videos have gained significant attention and are quickly emerging as the popular multimedia format for virtual reality experiences. All formats of videos (conventional and 360 degrees) undergo processing, compression, and transmission across dynamic wireless channels with restricted bandwidth to facilitate the streaming services. This causes video impairments, leading to quality degradation and poses challenges for the content providers in delivering good Quality-of-Experience (QoE) to the viewers. The QoE is a prominent subjective measure of quality, which has become a crucial component in assessing multimedia services and operations. So, there has been a growing preference for QoE-aware multimedia services over heterogeneous networks with a need to address design issues like how to evaluate and quantify end-to-end QoE. Efficient multimedia streaming techniques can improve the service quality while dealing with dynamic network and end-user challenges. A paradigm shift in user-centric multimedia services is envisioned with a focus on Machine Learning (ML) based QoE modeling and streaming strategies. This survey paper presents a comprehensive overview of the overall and continuous, time varying QoE modeling for the purpose of QoE management in multimedia services. It also examines the recent research on intelligent and adaptive multimedia streaming strategies, with a special emphasis on ML based techniques for video (conventional and 360 degrees) streaming. This paper discusses the overall and continuous QoE modeling to optimize the end-user viewing experience, efficient video streaming with a focus on user- centric strategies, associated datasets for modeling and streaming, along with existing shortcoming and open challenges.