Network Characterization and Perceptual Evaluation of Skype Mobile Videos. Jana, S, Pande, A, Chan, A, & Mohapatra, P In 22nd International Conference on Computer Communications and Networks (ICCCN), 2013. abstract bibtex We characterize the performance of both video and network layer properties of Skype, the most popular video telephony application. The performance in both mobile and stationary scenarios is investigated; considering network characteristics such as packet loss, propagation delay, available bandwidth and their effects on the perceptual video quality, measured using spatial and temporal no-reference video metrics. Based on 200+ live traces, we study the performance of this mobile video telephony application. We model video quality as a function of input network parameters and derive a feed-forward Artificial-Neural-Network that accurately predicts video quality given network conditions (0.0206 < MSE <0.570). The accuracy of this model improves significantly by incorporating end-user mobility as an input to the model.
@inproceedings{ SkypeICCCN,
title = {Network Characterization and Perceptual Evaluation
of Skype Mobile Videos},
year = {2013},
author = {S Jana and A Pande and A Chan and P Mohapatra},
year = {2013},
keywords = {WirelessNetworks,MultimediaCodingandCommunications,MachineLearning},
booktitle = {22nd International Conference on Computer Communications and Networks (ICCCN) },
abstract = {We characterize the performance of both video
and network layer properties of Skype, the most popular
video telephony application. The performance in both mobile
and stationary scenarios is investigated; considering network
characteristics such as packet loss, propagation delay, available
bandwidth and their effects on the perceptual video quality,
measured using spatial and temporal no-reference video metrics.
Based on 200+ live traces, we study the performance of this
mobile video telephony application. We model video quality as a
function of input network parameters and derive a feed-forward
Artificial-Neural-Network that accurately predicts video quality
given network conditions (0.0206 < MSE <0.570). The accuracy
of this model improves significantly by incorporating end-user
mobility as an input to the model.}
}
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