Back to normal? Impact of temporally increasing network disturbances on QoE

Shaikh, Junaid; Fiedler, Markus; Paul, Pangkaj Chandra; Egger, Sebastian; Guyard, Frédéric

doi:10.1109/glocomw.2013.6825154

Cited by 7 publications

(7 citation statements)

References 15 publications

(9 reference statements)

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“…While the effect has been studied in smaller scale [42], [43], long term aspects are unclear. We hypothesized that age and previous experiences are related.…”

Section: Discussionmentioning

confidence: 99%

“…This effect has been studied for short time frames for Web QoE [42] [43] in which it has been shown that after experiencing bad quality, participants reported a worse QoE even after the quality was back to normal. In relation to this, age has been shown to play a role in QoE, with elderly people reporting more problems in the usage of mobile phones, more skepticism towards new technology and a later adoption rate [44] (albeit differences in usage would often disappeared after elderly got more acquainted with the devices [45]).…”

Section: B Effect Of Context and User Factors On Qoementioning

confidence: 99%

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Towards Individual QoE for Multiparty Videoconferencing

Schmitt

Redi

Bulterman

et al. 2018

IEEE Trans. Multimedia

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Abstract-Video-conferencing is becoming an essential part in everyday life. The visual channel allows for interactions which were not possible over audio-only communication systems such as the telephone. However, being a de-facto over-the-top service, the quality of the delivered video-conferencing experience is subject to variations, dependent on network conditions. Video-conferencing systems adapt to network conditions by changing for example encoding bitrate of the video. For this adaptation not to hamper the benefits related to the presence of a video channel in the communication, it needs to be optimized according to a measure of the Quality of Experience (QoE) as perceived by the user. The latter is highly dependent on the ongoing interaction and individual preferences, which have hardly been investigated so far. In this paper, we focus on the impact video quality has on conversations that revolve around objects that are presented over the video channel. To this end we conducted an empirical study where groups of 4 people collaboratively build a Lego® model over a video-conferencing system. We examine the requirements for such a task by showing when the interaction, measured by visual and auditory cues, changes depending on the encoding bitrate and loss. We then explore the impact that prior experience with the technology and affective state have on QoE of participants. We use these factors to construct predictive models which double the accuracy compared to a model based on the system factors alone. We conclude with a discussion of how these factors could be applied in real world scenarios.Index Terms-Multi-Party video conferencing, Quality of Experience, Over-the-top, subjective quality, quality metrics, user study

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“…While the effect has been studied in smaller scale [42], [43], long term aspects are unclear. We hypothesized that age and previous experiences are related.…”

Section: Discussionmentioning

confidence: 99%

Section: B Effect Of Context and User Factors On Qoementioning

confidence: 99%

Towards Individual QoE for Multiparty Videoconferencing

Schmitt

Redi

Bulterman

et al. 2018

IEEE Trans. Multimedia

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“…This is likely to be explained by the interruption of the flow during an ongoing interaction. Shaikh et al [39] found for consecutive web pages that increasing the delay immediately reduces following retrospective judgments. If the delay is decreased afterwards again, following retrospective judgments remain affected.…”

Section: Perceived Quality Under Varying Performancementioning

confidence: 99%

Multi-episodic perceived quality for one session of consecutive usage episodes with a speech telephony service

et al. 2017

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The impact of varying performance and its effect on the perceived quality is an important aspect of quality of experience. Especially for service providers, it is important to understand how the perceived quality of a user, who is interacting with their services repeatedly, evolves. Repeateduse of a service is actually common for telecommunication services, such as speech telephony. For telecommunication services, it is very likely that a user encounters varying performance, for example due to varying network load conditions. For repeated-use taking place in a usage period covering several days, initial work on the formation process of the so-called multi-episodic perceived quality (i.e., distinct, meaningful interactions, denoted as usage episodes) has been conducted. In this paper, we present our work investigating the multi-episodic perceived quality for usage episodes taking place in one session (i.e., in a time frame of up to 45 min) with a speech telephony service. We investigate two aspects and their impact on the multi-episodic perceived quality: (a) increasing the number of low performance (LP) usage episodes and (b) varying the temporal position of LP usage episodes towards the assessment of multi-episodic perceived quality. Additionally, the impact of the user's behavioral freedom (i.e., two-party conversation vs. 3rd-party listening) on the two investigated aspects is examined. With regard to (a), the results show that an increasing number of LP usage episodes lead to a reduced multi-episodic perceived quality until saturation is reached. No impact of the user's behavioral freedom was found. For (b), an impact of the varied behavioral freedom was observed. Varying the temporal position had an impact on the multi-episodic perceived quality for two-party conversation. For 3rd-party-listening, this could only be observed in one out of two cases.

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“…Another study showed that the user-QoE drops significantly over time when the Page Load Time grows but it does not recover completely after the network problem is resolved [5]. In [6], authors show that the user satisfaction level breaks when waiting times exceed 10 seconds in a single session.…”

Section: Introductionmentioning

confidence: 99%