Perceptual QoE based resource allocation for mobile 3D video communications

Danish, Emad; Fernando, Anil; Abdul-Hameed, O; Alshamrani, Mazin; Kondoz, A.M.

doi:10.1109/icce.2014.6776083

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…Adaptive power control is part of link adaptation strategy, particularly explicit link adaptation. Link adaptation techniques have gained increasing attention for improving the quality of wireless multimedia communication schemes [3][4][5][6][7][8][9][10][11][12][13]. In explicit link adaptation, transmission parameters such as the signal power, the modulation constellation, or the amount of redundancy provided for error control are explicitly adjusted to compensate for the variations in channel conditions.…”

Section: Introductionmentioning

confidence: 99%

Adaptive power link adaptation on DVB-T system based on picture quality feedback

Kusuma¹,

Rahmanto²,

Haryatmi³

2019

IJECE

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<p>In digital television systems such as DVB-T, service provider has difficulties to observe the quality of picture reception in the viewers’ television. This is due to the unavailability of quality feedback sent from viewers’ devices to the service provider. Therefore, this research proposes link adaptation method in DVB-T system based on image quality measurement at recipient side, so that service provider may adjust the transmission power in real-time to improve the image quality. Quality metric used in this research is human perception- based no-reference image quality metric, which does not need the presence of the reference frame. The quality assessment is focused on the severeness of blocking artifact, which is the dominant artifacts in MPEG video. The numerical results have shown that power adaptation could maintain good picture quality as well as transmission power efficiency at the same time on the digital television transmission system. The proposed scheme is also suitable for other DVB system as well as various digital television system standards.</p>

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Section: Introductionmentioning

confidence: 99%

Adaptive power link adaptation on DVB-T system based on picture quality feedback

Kusuma¹,

Rahmanto²,

Haryatmi³

2019

IJECE

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“… For service charging, QoE could be used as a criterion for quality-based billing that employs differentiated charging schemes in real-time.  More efficient QoE-based resource utilization can be achieved in terms of bandwidth utilization and power consumption [7]. QoE prediction, however, requires a firm understanding of those QoS factors that are the most influential on QoE [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Prediction of perceptual quality for mobile video using fuzzy inference systems

Alreshoodi

Danish

Woods

et al. 2015

IEEE Trans. Consumer Electron.

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“…Noncontrollable parameters, such as packet loss, are assumed to be reported back from the user to the content provider through auxiliary instant feedback channels. For resource allocation, more efficient bandwidth utilization and power consumption can be achieved [3], along with better balanced allocation of resources based on QoE in a multiuser environment. Moreover, for service charging, along with the development of new charging models, service providers would offer advanced schemes such that users are charged based on the level of quality perceived.…”

Section: Introductionmentioning

confidence: 99%

A hybrid prediction model for video quality by QoS/QoE mapping in wireless streaming

Danish

Fernando

Alreshoodi

et al. 2015

2015 IEEE International Conference on Communication Workshop (ICCW)

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In the video streaming arena, and especially within the wireless transmission domain, measuring users' quality of experience (QoE) has become a pressing issue for it offers several benefits to both the service provider and the end user. However, available measurement techniques that adopt a full reference model (FR) are impractical in real-time transmission scenarios since they require the original video sequence available at the receiver's end. Hence, no-reference (NR) models fill this gap by providing less accurate measurement but sufficiently reliable for real-time video streaming. In this paper, we propose and evaluate a hybrid no-reference prediction model for the perceptual quality of video in the wireless domain. The model is based on fuzzy inference systems (FIS), and exploits several key parameters from both the application layer and physical layer. Hence the model is realized by means of mapping quality of service to quality of experience (QoS/QoE). The model is evaluated in contrast to random neural networks (RNN), and simulation results show considerable prediction accuracy of the model with a correlation coefficient of 92.17% and 0.1098 root mean square error.

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Perceptual QoE based resource allocation for mobile 3D video communications

Cited by 5 publications

References 4 publications

Adaptive power link adaptation on DVB-T system based on picture quality feedback

Adaptive power link adaptation on DVB-T system based on picture quality feedback

Prediction of perceptual quality for mobile video using fuzzy inference systems

A hybrid prediction model for video quality by QoS/QoE mapping in wireless streaming

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