QoE Modeling for HTTP Adaptive Video Streaming–A Survey and Open Challenges

Abstract

With the recent increased usage of video services, the focus has recently shifted from the traditional quality of service-based video delivery to quality of experience (QoE)-based video delivery. Over the past 15 years, many video quality assessment metrics have been proposed with the goal to predict the video quality as perceived by the end user. HTTP adaptive streaming (HAS) has recently gained much attention and is currently used by the majority of video streaming services, such as Netflix and YouTube. HAS, using reliable transport protocols, such as TCP, does not suffer from image artifacts due to packet losses, which are common in traditional streaming technologies. Hence, the QoE models developed for other streaming technologies alone are not sufficient. Recently, many works have focused on developing QoE models targeting HAS-based applications. Also, the recently published ITU-T Recommendation series P.1203 proposes a parametric bitstream-based model for the quality assessment of progressive download and adaptive audiovisual streaming services over a reliable transport. The main contribution of this paper is to present a comprehensive overview of recent and currently undergoing works in the field of QoE modeling for HAS. The HAS QoE models, influence factors, and subjective test methodologies are discussed, as well as existing challenges and shortcomings. The survey can serve as a guideline for researchers interested in QoE modeling for HAS and also discusses possible future work.INDEX TERMS HTTP adaptive streaming, QoE modeling, TCP, video quality assessment.

A QoE-Oriented Uplink Allocation for Multi-UAV Video Streaming

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QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey

“…QoE modeling, QoE monitoring and management [25], QoEcentric management of adaptive video streaming services [31] To some extent ICN only [31], MEC only [25] AR/VR & multisensory Barman and Martini [30] Yes ** QoE in New Domains: We refer to aspects of QoE in new domains such as AR/VR, mulsemedia and gaming video streaming applications. It is to be noted that with the exception of Skorin-Kapov et al [25] and Petrangeli et al [31] which discuss AR/VR & multisensory applications, other survey papers are limited in this aspect which is covered in this work.…”

Quality of Experience for Streaming Services: Measurements, Challenges and Insights

“…The streaming service has adopted a new protocol that answers to the massive demand on network requirements like bandwidth, entitled DASH [159], [160]. It is proved to adapt the quality of the requested video, based on the current bandwidth and devices qualification, but it is affected by many factors based on [161], [162], initial delay, stalling and level variation (frame rate, bit-rate and resolution), besides other factors like video length and the number of motions in the video. Consequently, to derive an effective the trade-off between the network variations and dynamic videos streaming behavior, they [163] introduce a queue-based model to analyze the video buffer (GI/GI/1 queue) with pq-policy (pausing or continuing the video download) using discrete-time analysis.…”