Modeling the QoE of Rate Changes in Skype/SILK VoIP Calls

Chen, Shannon; Chu, Cing-Yu; Yeh, Su‐Ling; Chu, Hao-Hua; Huang, Polly

doi:10.1109/tnet.2013.2286624

Cited by 11 publications

(5 citation statements)

References 22 publications

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“…Note that prior work [15] has shown that 30 people are sufficient to generate significant statistics. 60 is twice the amount to further enhance the confidence and so that the total payment to Amazon and the workers are controlled within our budget.…”

Section: User Demographymentioning

confidence: 99%

Lab experiment vs. crowdsourcing

Yen

Chu

Yeh

et al. 2013

Proceedings of the 9th Asian Internet Engineering Conference

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To deliver voice over the Internet in a cost-effective way, it is essential to quantify the quality of user experience (i.e., QoE) of a voice service at various provisioning levels. Conducting user studies is an inevitable step facilitating quantitative studies of QoE. The two experimental methods -lab experiment vs. crowdsourcing via Amazon Mechanical Turk [1] -are compared in this study. We find that, for the study of Skype call quality, the crowdsourcing approach stands out in terms of efficiency and user diversity, which in turn strengthens the robustness and the depth of the analysis.

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Section: User Demographymentioning

confidence: 99%

Lab experiment vs. crowdsourcing

Yen

Chu

Yeh

et al. 2013

Proceedings of the 9th Asian Internet Engineering Conference

Self Cite

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“…The research community has established a body of work that investigated several of the influencing factors [1] for QoE in video-conferencing. In the main focus are the system factors, with aspects like audio and video quality [6,14,40,43,62], audiovisual synchronization [58] and delay [51,59]. Further studies regarding non-technical influencing factors have looked into interaction [20,47,59], the user state, such as engagement [52] and contextual factors like different devices [10,66].…”

Section: Subjective Studies Investigating Qoe In Video-conferencingmentioning

confidence: 99%

The contrast effect: QoE of mixed video-qualities at the same time

2018

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In desktop multi-party video-conferencing videostreams of participants are delivered in different qualities, but we know little about how such composition of the screen affects the quality of experience. Do the different videostreams serve as indirect quality references and the perceived video quality is thus dependent on other streams in the same session? How is the relation between the perceived qualities of each stream and the perceived quality of the overall session? To answer these questions we conducted a crowdsourcing study, in which we gathered over 5000 perceived quality ratings of overall sessions and individual streams. Our results show a contrast effect: high quality streams are rated better when more low quality streams are co-present, and vice versa. In turn, the quality perception of an overall session can increase significantly by exchanging one low quality stream with a high quality one. When comparing the means of individual and overall ratings we can further observe that users are more critical when asked for individual streams than for an overall rating. However, the results show that while contrast effect exists, the effect is not strong enough, to optimize the experience by lowering the quality of other participants.

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“…The correlation between the stability of the connection and the QoE is considered in [22], [23]; we do not consider the variability of network conditions in our work but as long as variability is measurable, it can be introduced in our methodology and used to calibrate the model.…”

Section: Related Workmentioning

confidence: 99%

From network-level measurements to expected quality of experience: The Skype use case

Spetebroot

Afra

Aguilera

et al. 2015

2015 IEEE International Workshop on Measurements &Amp; Networking (M&N)

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Modern Internet applications rely on rich multimedia contents making the quality of experience (QoE) of end users sensitive to network conditions. Several models were developed in the literature to express QoE as a function of measurements carried out on the traffic of the applications themselves. In this paper, we propose a new methodology based on machine learning able to link expected QoE to network and device level measurements outside the applications' traffic. This direct linking to network and device level measurements is important for the prediction of QoE. We prove the feasibility of the approach in the context of Skype. In particular, we derive and validate a model to predict the Skype QoE as a function of easily measurable network performance metrics. One can see our methodology as a new way of performing measurements in the Internet, where instead of expressing the expected performance in terms of network and device level measurements that only specialists can understand, we express performance in clear terms related to expected quality of experience for different applications.

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Modeling the QoE of Rate Changes in Skype/SILK VoIP Calls

Cited by 11 publications

References 22 publications

Lab experiment vs. crowdsourcing

Lab experiment vs. crowdsourcing

The contrast effect: QoE of mixed video-qualities at the same time

From network-level measurements to expected quality of experience: The Skype use case

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