[Retracted article] Improving Quality of Adaptive Video by Traffic Prediction with (F)ARIMA Models
Notice of retraction: Regarding the article by A. Biernacki, "Improving quality of adaptive video by traffic prediction with (F)ARIMA models," Journal of Communications and Networks, Volume 19, Issue 5, pp. 521-530. DOI: 10.1109/JCN.2017.000083,
after careful investigation of this paper by a duly constituted expert committee, Journal of communications and networks and PLOS ONE have determined that it is a duplicate submission.
The Journal of Communications and Networks, therefore, retracts the article.
"During the past years, adaptive video based on hypertext
transfer protocol HTTP has become very popular. Streaming
of the adaptive video relies heavily on an estimation of end-to-end
network throughput, which can be challenging especially in mobile
networks, where the capacity highly fluctuates. In this work,
we propose to predict the network throughput using its past measurements.
As the analysis shows, the network throughput forms a
long range-dependent process, thus, for the throughput prediction
we apply fractional ARIMA (FARIMA) model. Our approach does
not require any modifications to the network infrastructure or the
TCP stack. The predictions are performed for data traces obtained
from measurements of throughput of a real mobile network. As
the experiment shows, the obtained traffic model is able to enhance
the performance of an adaptive streaming algorithm. Compared to
the throughput predictors employed in contemporary systems dedicated
to adaptive video streaming, the proposed technique obtains
better results when taking into account effectiveness of network capacity
utilisation and stability of video play-out."