A Review of Predictive Quality of Experience Management in Video Streaming Services

Torres Vega, Maria; Perra, Cristian; De Turck, Filip; Liotta, Antonio

Abstract

Satisfying the requirements of devices and users of online video streaming services is a challenging task. It requires not only managing the network quality of service but also to exert real-time control, addressing the user's quality of experience (QoE) expectations. QoE management is an end-to-end process that, due to the ever-increasing variety of video services, has become too complex for conventional “reactive” techniques. Herein, we review the most significant “predictive” QoE management methods for video streaming services, showing how different machine learning approaches may be used to perform proactive control. We pinpoint a selection of the best suited machine learning methods, highlighting advantages and limitations in specific service conditions. The review leads to lessons learned and guidelines to better address QoE requirements in complex video services.

Citation

Torres Vega, M., Perra, C., De Turck, F., & Liotta, A. (2018). A Review of Predictive Quality of Experience Management in Video Streaming Services. IEEE Transactions on Broadcasting, 64(2), 432-445. https://doi.org/10.1109/tbc.2018.2822869

• Journal Article Type: Article
• Acceptance Date: Mar 27, 2018
• Online Publication Date: Apr 20, 2018
• Publication Date: 2018-06
• Deposit Date: Jul 29, 2019
• Publicly Available Date: Aug 2, 2019
• Journal: IEEE Transactions on Broadcasting
• Print ISSN: 0018-9316
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 64
• Issue: 2
• Pages: 432-445
• DOI: https://doi.org/10.1109/tbc.2018.2822869
• Keywords: Machine learning, quality of experience management, video streaming services
• Public URL: http://researchrepository.napier.ac.uk/Output/1995605
• Publisher URL: https://doi.org/10.1109%2Ftbc.2018.2822869