A review on Machine Learning based User-Centric Multimedia Streaming Techniques

TL;DR

This paper reviews machine learning techniques for user-centric multimedia streaming, focusing on QoE modeling and adaptive strategies to enhance user experience in dynamic wireless environments.

Contribution

It provides a comprehensive overview of ML-based QoE modeling and adaptive streaming strategies specifically for multimedia content, including 360° videos, highlighting recent research and challenges.

Findings

ML-based QoE models improve streaming quality

Adaptive strategies enhance user experience in wireless networks

Identifies open challenges and future research directions

Abstract

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$^o$ videos have gained popularity with the emerging virtual reality applications. All formats of videos (conventional and 360$^o$) 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 in delivering good Quality-of-Experience (QoE) to the viewers. The QoE is a prominent subjective quality measure to assess multimedia services. This requires end-to-end QoE evaluation. 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$^o$) 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.