The Impact of Adaptation Strategies on Perceived Quality of HTTP Adaptive Streaming

Egger, Sebastian; Gardlo, Bruno; Seufert, Michael; Schatz, Raimund

doi:10.1145/2676652.2676658

Cited by 32 publications

(25 citation statements)

References 21 publications

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“…[11] found that neither smooth nor abrupt had a significantly better performance, although results in [12] indicate that slow gradual down switching was preferred. Also [13] did not find a significant difference for quality increases. Only for down switching smooth switching performed slightly better in terms of QoE than abrupt switching.…”

Section: Related Workmentioning

confidence: 75%

Impact of intermediate layer on quality of experience of HTTP adaptive streaming

Seufert

Hosfeld

Sieber

2015

2015 11th International Conference on Network and Service Management (CNSM)

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HTTP Adaptive Streaming (HAS) adapts the video quality to the current network condition by switching between different quality layers. As HAS was shown to perform better than classical video streaming, it is becoming increasingly popular. Recent research showed that quality switch amplitude and time on layer have an impact on the Quality of Experience (QoE) of HAS. However, those studies focused only on adaptation between two layers so far. This work extends these findings by taking adaptation between three layers into account. Thereby, especially the impact of an intermediate layer on user perceived quality is investigated. Crowdsourcing experiments were conducted in order to collect subjective ratings for adaptation between three layers. The results indicate that the quality of each layer and the time on each layer are important QoE parameters. This encourages the usage of temporal pooling approaches for QoE prediction and QoE-aware traffic management. Therefore, mean pooling of per-frame metrics will be applied and its performance will be validated with the subjective crowdsourcing results.

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Section: Related Workmentioning

confidence: 75%

Impact of intermediate layer on quality of experience of HTTP adaptive streaming

Seufert

Hosfeld

Sieber

2015

2015 11th International Conference on Network and Service Management (CNSM)

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“…2) LabII and CSIII: The comprehensive technical report of these studies has been provided in [29]. Besides using different evaluation methodology, different adaptation sets were also used in two studies: In LabII, the video quality was varied along the compression dimension , whereas in CSIII the spatial resolution of the videos was varied.…”

Section: A Test Materials and Conditionsmentioning

confidence: 99%

Perceptual Quality of HTTP Adaptive Streaming Strategies: Cross-Experimental Analysis of Multi-Laboratory and Crowdsourced Subjective Studies

Tavakoli

Egger

Seufert

et al. 2016

IEEE J. Select. Areas Commun.

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Abstract-Today's packet-switched networks are subject to bandwidth fluctuations that cause for degradation of user experience of multimedia services. In order to cope with this problem, HTTP Adaptive Streaming (HAS) has been proposed in recent years as a video delivery solution for the future Internet and being adopted by an increasing number of streaming services such as Netflix and Youtube. HAS enables service providers to improve users' Quality of Experience (QoE) and network resource utilization by adapting the quality of the video stream to the current network conditions. However, the resulting timevarying video quality caused by adaptation introduces a new type of impairment and thus novel QoE research challenges. Despite of various recent attempts to investigate these challenges, many fundamental questions regarding HAS perceptual performance are still open. In this paper, the QoE impact of different technical adaptation parameters including chunk length, switching amplitude, switching frequency and temporal recency are investigated. In addition, the influence of content on perceptual quality of these parameters is analyzed. To this end, a large number of adaptation scenarios have been subjectively evaluated in four laboratory experiments and one crowdsourcing study. Statistical analysis of the combined dataset reveals results that partly contradict widely held assumptions and provide novel insights in perceptual quality of adapted video sequences, e.g. interaction effects between quality switching direction (up/down) and switching strategy (smooth/abrupt). The large variety of experimental configurations across different studies ensures the consistency and external validity of the presented results that can be utilized for enhancing the perceptual performance of adaptive streaming services.

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“…Reference [2] investigates the impact of quality switches on the QoE in comparison to stalling events. Their results show that smooth switching only performs slightly better than abrupt switching.…”

Section: Background and Related Workmentioning

confidence: 99%

“…This may lead to a high number of quality switches, depending on the adaptation strategy. Even though stalling can be avoided in most cases, a high number of quality switches may also have a negative impact on the Quality of Experience (QoE) [1], [2]. A very passive adaptation strategy plays the video on a lower quality in order to keep the number of switches at a minimum.…”

Section: Introductionmentioning

confidence: 99%

Keep Calm and Don’t Switch: About the Relationship Between Switches and Quality in HAS

Moldovan

Hagn

Sieber

et al. 2017

2017 29th International Teletraffic Congress (ITC 29)

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Abstract-Video streaming has established itself as the main method to consume multimedia content on the Internet. The most widespread technique is HTTP Adaptive Streaming which is used by large video service platforms such as YouTube and Netflix. In order to guarantee a high QoE, different adaptation strategies have been developed that lead to different video playout patterns. While some strategies may be more aggressive than others and often adapt the video quality, it is not clear which strategy is the best.In this paper, we want to identify the trade-off between the average video quality and switches in the quality during playout. We do this with a user-centric view and try to optimize the adaptation depending on the user preference with a quadratic program. This work allows us to put existing and future video adaptation algorithms in perspective with respect to user preferences. Our results show that the video quality can already be increased greatly by allowing few switches while more switches lead to diminishing gains. This is a novel discovery that is important for user-centric QoE-management which is of high interest for ISPs and video service providers.

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The Impact of Adaptation Strategies on Perceived Quality of HTTP Adaptive Streaming

Cited by 32 publications

References 21 publications

Impact of intermediate layer on quality of experience of HTTP adaptive streaming

Impact of intermediate layer on quality of experience of HTTP adaptive streaming

Perceptual Quality of HTTP Adaptive Streaming Strategies: Cross-Experimental Analysis of Multi-Laboratory and Crowdsourced Subjective Studies

Keep Calm and Don’t Switch: About the Relationship Between Switches and Quality in HAS

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