A Hybrid Contextual User Perception Model for Streamed Video Quality Assessment

Diallo, Mamadou Tourad; Marechal, Nicolas; Afifi, Hossam

doi:10.1109/ism.2013.104

Cited by 4 publications

(2 citation statements)

References 9 publications

(9 reference statements)

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“…With respect to context, studies have looked at physical surroundings (e.g. in public with a mobile vs at home on a computer) [40] or economic factors [41].…”

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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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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“…With respect to context, studies have looked at physical surroundings (e.g. in public with a mobile vs at home on a computer) [40] or economic factors [41].…”

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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“…The main goal of QoEMU is to detect problematic scenarios and react applying different actions to mitigate adverse effects, ensuring a minimum overall degradation of the QoE. It is known that the QoE depends on the end user's device [15]. In this work, mobile phones were considered.…”

Section: Introductionmentioning

confidence: 99%

A Quality of Experience Management Framework for Mobile Users

Algar

Diego

Fernández-Isabel

et al. 2019

Wireless Communications and Mobile Computing

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Voice transmission is no longer the main usage of mobile phones. Data transmissions, in particular Internet access, are very common actions that we might perform with these devices. However, the spectacular growth of the mobile data demand in 5G mobile communication systems leads to a reduction of the resources assigned to each device. Therefore, to avoid situations in which the Quality of Experience (QoE) would be negatively affected, an automated system for degradation detection of video streaming is proposed. This approach is named QoE Management for Mobile Users (QoEMU). QoEMU is composed of several modules to perform a real-time analysis of the network traffic, select a mitigation action according to the information of the traffic and to some predefined policies, and apply these actions. In order to perform such tasks, the best Key Performance Indicators (KPIs) for a given set of video traces are selected. A QoE Model is trained to define a global QoE for the set of traces. When an alert regarding degradation in the quality appears, a proper mitigation plan is activated to mitigate this situation. The performance of QoEMU has been evaluated over a degradation situation experiments with different video users.

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