A Cumulative Quality Model for HTTP Adaptive Streaming

Tran, Huyen T. T.; Ngoc, Nam Pham; Hoβfeld, Tobias; Thang, Truong Cong

doi:10.1109/qomex.2018.8463414

Cited by 12 publications

(9 citation statements)

References 40 publications

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“…Studies [10]- [13] focus on IPTV. Studies [14]- [16] focus on QoE modelling in HTTP streaming environments and we also refer readers to [17] for a survey of this area. ITU-T frameworks on subjective and objective testing for media delivery also exist [18]- [21].…”

Section: Related Workmentioning

confidence: 99%

Benchmarking Video Service Quality: Quantifying the Viewer Impact of Loss-Related Impairments

Karthikeyan

Allan

Nauck

et al. 2020

IEEE Trans. Netw. Serv. Manage.

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We present the first empirical study of the impact of loss-related errors on TV viewing engagement across disparate platforms, delivery technologies and performance measures. Our dataset comprises anonymised video viewing sessions and data about quality of delivery from a content service provider with a nationwide customer base. We study buffering events on streaming apps, mild and severe packet loss errors on multicastdelivered IPTV to a Set-Top-Box (STB) and signal strength errors on Digital Terrestrial TV. Since these metrics cannot be directly compared to each other, we use engagement as our proxy measure. We first characterise the relationship between each impairment and viewing engagement, investigating confounding factors such as type of content, asset length and connection type. We conclude that the loss of engagement due to poor quality delivery is incurred immediately for on-demand content and in the long-term for live content. We rank impairments across platforms by their impact on engagement.

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Section: Related Workmentioning

confidence: 99%

Benchmarking Video Service Quality: Quantifying the Viewer Impact of Loss-Related Impairments

Karthikeyan

Allan

Nauck

et al. 2020

IEEE Trans. Netw. Serv. Manage.

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“…For those reasons, the idea of cumulative assessment needs to be considered. In [2], the cumulative perceptual quality was assessed by leveraging the concept of a sliding window. The work in [32] evaluated the cumulative QoE driven by video quality, bitrate switching, and rebuffering events aligning with the exponential decay of human memory.…”

Section: Related Workmentioning

confidence: 99%

“…The work in [32] considered the forgetting behavior in their cumulative QoE model, but did not employ the effect of primacy and recency. On the contrary, the authors of [2] only investigated the primacy and recency effect. Besides the above influence factors, the effect of video content was also concerned by contemporary works [9,13,[23][24][25]35].…”

Section: Related Workmentioning

confidence: 99%

“…To correctly determine the end user quality of experience ( QoE) for adaptive streaming services and subsequently perform a QoE based network control and management, there exists a need for the development of reliable QoE models [1] to produce a highly accurate QoE prediction either at any moment or at the end of a streaming session. This requirement links to the phenomenon of cumulative assessment where QoE can be cumulatively estimated from the time when the viewer starts watching a streaming video content to any moment of the streaming session [2]. However, such a method requires a complex quantification of multiple effects of QoE influence factors, especially human-related factors.…”

Section: Introductionmentioning

confidence: 99%

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Modeling of Cumulative QoE in On-Demand Video Services: Role of Memory Effect and Degree of Interest

et al. 2019

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The growing demand on video streaming services increasingly motivates the development of a reliable and accurate models for the assessment of Quality of Experience (QoE). In this duty, human-related factors which have significant influence on QoE play a crucial role. However, the complexity caused by multiple effects of those factors on human perception has introduced challenges on contemporary studies. In this paper, we inspect the impact of the human-related factors, namely perceptual factors, memory effect, and the degree of interest. Based on our investigation, a novel QoE model is proposed that effectively incorporates those factors to reflect the user's cumulative perception. Evaluation results indicate that our proposed model performed excellently in predicting cumulative QoE at any moment within a streaming session.

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“…For traditional video, two key QoE aspects are perceptual quality and acceptability, which have been considered in many previous studies [4][5][6][7]. Hence, in this study, we also investigate the two QoE aspects in the context of 360 video.…”

Section: Introductionmentioning

confidence: 99%

A Subjective Study on User Perception Aspects in Virtual Reality

et al. 2019

Self Cite

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Three hundred and sixty degree video is becoming more and more popular on the Internet. By using a Head-Mounted Display, 360-degree video can render a Virtual Reality (VR) environment. However, it is still a big challenge to understand Quality of Experience (QoE) of 360-degree video since user experience during watching 360-degree video is a very complex phenomenon. In this paper, we aim to investigate four QoE aspects of 360-degree video, namely, perceptual quality, presence, cybersickness, and acceptability. In addition, four key QoE-affecting factors of encoding parameters, content motion, rendering device, and rendering mode are considered in our study. To the best of our knowledge, this is the first work that covers a large number of factors and QoE aspects of 360-degree video. In this study, a subjective experiment is conducted using 60 video versions generated from three original 360-degree videos. Based on statistical analysis of the obtained results, various findings on the impacts of the factors on the QoE aspects are provided. In particular, regarding the impacts of encoding parameters, it is found that the difference of QoE is negligible between video versions encoded at 4 K and 2.5 K resolutions. Also, it is suggested that 360-degree video should not be encoded at HD resolution or lower when watching in VR mode using Head Mounted Display. In addition, the bitrate for good QoE varies widely across different video contents. With respect to the content motion factor, its impact is statistically significant on the perceptual quality, presence, and cybersickness. In a comparison of two rendering device sets used in this study, there is no statistically significant difference found for the acceptability and cybersickness. However, the differences of the perceptual quality and presence are indicated to be statistically significant. Regarding the rendering mode, a comparison between VR and non-VR modes is also conducted. Although the non-VR mode always achieves higher perceptual quality scores and higher acceptability rates, more than a half of the viewers prefer the VR mode to the non-VR mode when watching versions encoded at the resolutions of fHD or higher. By contrast, the non-VR mode is preferred at the HD resolution.

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A Cumulative Quality Model for HTTP Adaptive Streaming

Cited by 12 publications

References 40 publications

Benchmarking Video Service Quality: Quantifying the Viewer Impact of Loss-Related Impairments

Benchmarking Video Service Quality: Quantifying the Viewer Impact of Loss-Related Impairments

Modeling of Cumulative QoE in On-Demand Video Services: Role of Memory Effect and Degree of Interest

A Subjective Study on User Perception Aspects in Virtual Reality

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