2016 IEEE International Conference on Consumer Electronics (ICCE) 2016
DOI: 10.1109/icce.2016.7430591
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Cross-layer QoE prediction for mobile video based on random neural networks

Abstract: Based on random neural networks, a cross-layer prediction model is proposed for estimating the perceptual quality of mobile video in no reference mode. The model exploits key parameters affecting video quality. Simulation results show considerable predictability performance with R-squared correlation of 0.90 and 0.39 root mean squared error.

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Cited by 3 publications
(3 citation statements)
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“…The prediction models for mobile video streaming [143]- [149] evaluate the impact of cross-layer IFs, including QoS components from the application layer as well as the physical layer. In [143], a no reference cross-layer end-to-end estimation model for mobile video perceptual quality is presented, based on random neural networks.…”
Section: A Video Streamingmentioning
confidence: 99%
See 1 more Smart Citation
“…The prediction models for mobile video streaming [143]- [149] evaluate the impact of cross-layer IFs, including QoS components from the application layer as well as the physical layer. In [143], a no reference cross-layer end-to-end estimation model for mobile video perceptual quality is presented, based on random neural networks.…”
Section: A Video Streamingmentioning
confidence: 99%
“…The prediction models for mobile video streaming [143]- [149] evaluate the impact of cross-layer IFs, including QoS components from the application layer as well as the physical layer. In [143], a no reference cross-layer end-to-end estimation model for mobile video perceptual quality is presented, based on random neural networks. In [144] an online QoE prediction model is proposed, capable of classifying user perception of video streaming services, based on incremental multiclass SVM (multiclass-iSVM) algorithm, which examines the efficacy of incremental learning in handling large scale dynamic data and improving QoE prediction accuracy.…”
Section: A Video Streamingmentioning
confidence: 99%
“…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%