On user-centric analysis and prediction of QoE for video streaming using empirical measurements

Plakia, Maria; Katsarakis, Michalis; Charonyktakis, Paulos; Papadopouli, Maria; Markopoulos, Ioannis

doi:10.1109/qomex.2016.7498962

Cited by 6 publications

(7 citation statements)

References 29 publications

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“…The negative impact of RBs and startup delays has been also reported in other studies (e.g. [14,18,19]). These papers agree that the large duration and number of RBs are critical parameters on QoE.…”

Section: Introductionsupporting

confidence: 84%

“…Table 1: A brief summary of our key findings. The negative role of the number of RB and of the large RB duration has been also reported in other studies [19,14,23,18,4,32]. The negative impact of the BR changes has been also shown [1,23,17].…”

Section: 6supporting

confidence: 66%

“…Some of our findings are consistent with the results of previous research studies. The negative role of the number of RB and large RB duration has been also reported in several other studies [19,14,23,18,4,32]. Moreover, the negative impact of BR changes has been demonstrated in previous studies (for YouTube sessions in [23], in controlled field studies with distorted video [17]).…”

Section: Introductionsupporting

confidence: 71%

“…Monitoring tools that collect various QoS measurements, such as player state, statistics of buffering events, and video quality level have been developed [6,31,23,24,19]. The analysis of collected QoS measurements to infer the QoE of video streaming, and specifically of YouTube, a major source of traffic worldwide, has been presented in [23,24,14,7,25,4].…”

Section: Related Workmentioning

confidence: 99%

“…The relation of QoS and QoE parameters has been examined in various papers, such as in [29,34], specifically by applying the Weber Fechner Law and IQX [12,13], simple regression models [9,5], full-reference signal processing techniques (e.g., VQM [26]), or machine-learning algorithms [22,30,21,15,28,19,4].…”

Section: Related Workmentioning

confidence: 99%

See 4 more Smart Citations

Should I stay or should I go: Analysis of the impact of application QoS on user engagement in YouTube

Plakia¹,

Tzamousis²,

Asvestopoulou³

et al. 2019

Preprint

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To improve the quality of experience (QoE), especially under moderate to high traffic demand, it is important to understand the impact of the network and application QoS on user experience. This paper comparatively evaluates the impact of impairments, their intensity and temporal dynamics, on user-engagement in the context of video streaming. The analysis employed two large YouTube datasets. To characterize the user engagement and the impact of impairments, several new metrics were defined. We assessed whether or not there is a statistically significant relationship between different types of impairments and QoE and user engagement metrics, taking into account not only the characteristics of the impairments but also the covariates of the session (e.g., video duration, mean datarate). After observing the relationships across the entire dataset, we tested whether these relationships also persist under specific conditions with respect to the covariates. The introduction of several new metrics and of various covariates in the analysis are two innovative aspects of this work. We found that the number of negative bitrate changes (BR-) is a stronger predictor of abandonment than rebufferrings (RB). Even positive bitrate changes (BR+) are associated with increases in abandonment. Specifically, BR+ in low resolution sessions is not well-received. Temporal dynamics of the impairments have also an impact: a BR-that follows much later a RB appears to be perceived as a worse impairment than a BR-that occurs immediately after a RB. These results can be used to guide the design of the video streaming adaptation as well as suggest which parameters should be varied in controlled field studies.

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Section: Introductionsupporting

confidence: 84%