Towards a comprehensive framework for QOE and user behavior modelling

Reichl, Peter; Egger, Sebastian; Möller, Sebastian; Kilkki, Kalevi; Fiedler, Markus; Hoßfeld, Tobias; Tsiaras, Christos; Asrese, Alemnew Sheferaw

doi:10.1109/qomex.2015.7148138

Cited by 45 publications

(40 citation statements)

References 7 publications

(12 reference statements)

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“…It has been addressed that the model has some short comings when it comes to interactivity and QoE [11] and an approach to better describe the relation between user behavior, user state and QoE has been presented [14]. Further the quality formation process from [2] has been refined for the multiparty context [25].…”

Section: Related Workmentioning

confidence: 99%

“…Hence, interaction should also be accounted for, when modeling videoconferencing QoE. The 'Framework for QoE and User Behavior Modelling' [14] conceptualizes the reciprocal relationship of QoE, user state (e.g. mood) and user behavior.…”

mentioning

confidence: 99%

“…1. The figure is based on the model proposed in [14] and shows the factors examined in this paper and their relation. Our hypothesis is that the context will shape, together with the user and the system factors, the user behavior.…”

mentioning

confidence: 99%

“…depending on engagement). In addition, and differently from [14], we consider also the possibility that user behavior can be influenced by system factors. Finally, user, context, and system factors along with user behavior will influence the users QoE, which in turn will influence user behavior and current state of the user.…”

mentioning

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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Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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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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“…However, as pointed out in the literature review section, video quality assessment on mobile devices has been done with low resolution videos and using only the H.264 and MPEG-2/4 video codecs. In order to estimate the video quality from subjective metrics like MOS feedforward type ANNs are most commonly used [58][59][60][61]. This is the reason why we decided to use an ANN technique for this paper keeping in mind the current research gaps and trying to overcome those.…”

Section: Ann Based Video Modelmentioning

confidence: 99%

A No-Reference Modular Video Quality Prediction Model for H.265/HEVC and VP9 Codecs on a Mobile Device

Pal

Vanijja

2017

Advances in Multimedia

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We propose a modular no-reference video quality prediction model for videos that are encoded with H.265/HEVC and VP9 codecs and viewed on mobile devices. The impairments which can affect video transmission are classified into two broad types depending upon which layer of the TCP/IP model they originated from. Impairments from the network layer are called the network QoS factors, while those from the application layer are called the application/payload QoS factors. Initially we treat the network and application QoS factors separately and find out the 1 : 1 relationship between the respective QoS factors and the corresponding perceived video quality or QoE. The mapping from the QoS to the QoE domain is based upon a decision variable that gives an optimal performance. Next, across each group we choose multiple QoS factors and find out the QoE for such multifactor impaired videos by using an additive, multiplicative, and regressive approach. We refer to these as the integrated network and application QoE, respectively. At the end, we use a multiple regression approach to combine the network and application QoE for building the final model. We also use an Artificial Neural Network approach for building the model and compare its performance with the regressive approach.

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Measuring Web Quality of Experience in Cellular Networks

Asrese

Walelgne

Bajpai

et al. 2019

Lecture Notes in Computer Science

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I am immensely thankful to my families and relatives for their unconditional love and support through my school life. Last but not least, I would like to thank my better half, Ichalem, for her love and everything else. This thesis is dedicated to my mother Tena Yihunie -who is my role model of perseverance and confidence -and Adisualem -youngest brother, who was born three weeks after this research was started.

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Towards a comprehensive framework for QOE and user behavior modelling

Cited by 45 publications

References 7 publications

Towards Individual QoE for Multiparty Videoconferencing

Towards Individual QoE for Multiparty Videoconferencing

A No-Reference Modular Video Quality Prediction Model for H.265/HEVC and VP9 Codecs on a Mobile Device

Measuring Web Quality of Experience in Cellular Networks

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