User-Experience-Oriented Fuzzy Logic Controller for Adaptive Streaming

Hou, Yonghong; Lin, Xue; Li, Shuo; Xing, Jiaming

doi:10.1093/comjnl/bxy010

Cited by 6 publications

(1 citation statement)

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“…Its objective is to maximize the Quality of Experience (QoE) of the user. New strategies are coming up regularly and are being discussed in the research community [4]- [11]. They with regard to their throughput forecasting behavior, which allows them to improve the played back video quality, while reducing video stallings and quality switches.…”

Section: Introductionmentioning

confidence: 99%

Computing QoE-Relevant Adaptive Video Streaming Metrics Using Discrete-Time Analysis

Schwarzmann

Breitbach

Zinner

2019

2019 22nd Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN)

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HTTP Adaptive Streaming (HAS) is the de-facto standard for video delivery over the Internet. Splitting the video clip into small segments and providing multiple quality levels per segment allows the client to dynamically adapt the quality to current network conditions. The performance of HAS, and as a consequence the user Quality of Experience (QoE), is influenced by a multitude of parameters. This includes adjustable settings like quality switching thresholds, the initial buffer level, or the maximum buffer, as well as video characteristics like segment duration or the variation of segment sizes along the video. Finding an appropriate tuning of those parameters still remains a challenge, which is mainly tackled by performing testbed measurements or simulative analysis. Due to the large problem space and the complex interactions of the involved influence factors, a holistic comparison of a multitude of parameter settings is extremely time intensive. To tackle this problem, we propose to enhance a GI/GI/1 system with pq-policy, which models video buffer behavior, with the capability to switch between different quality levels. This allows to investigate all relevant QoE influence factors for HAS-based video delivery. In a first evaluation, we illustrate the impact of different quality switching thresholds on the QoE influence factors for varying network conditions.

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Section: Introductionmentioning

confidence: 99%