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-toend 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.

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

Cristian Perra;Antonio Liotta
2018-01-01

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-toend 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.
2018
machine learning; quality of experience management; video streaming services
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/243876
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