Optimizing Quality of Experience for Long-Range UAS Video Streaming

Shirey, Russell; Rao, Sanjay; Sundaram, Shreyas

doi:10.1109/iwqos52092.2021.9521330

Cited by 4 publications

(1 citation statement)

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“…In the context of VR streaming from aerial users, such as drones, additional challenges arise due to their dynamic topology and limited energy resources [110], [111]. The channel quality and network throughput of aerial users are also influenced by their flight trajectory, necessitating the joint design of QoE-aware resource allocation and drone route selection mechanisms [112], [113].…”

Section: Qoe-aware Control and Communication Of Uavsmentioning

confidence: 99%

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

2019

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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.

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