Detail publikace

Predicting User QoE Satisfaction in Current Mobile Networks

Originální název

Predicting User QoE Satisfaction in Current Mobile Networks

Anglický název

Predicting User QoE Satisfaction in Current Mobile Networks

Jazyk

en

Originální abstrakt

Intensive competition between network operators as well as steady increase in mobile traffic call for additional investments into the networking infrastructure. Keeping current mobile networks profitable, the following criteria should be satisfied: end-user quality expectations need to be fulfilled on the one hand and service quality overprovisioning should be eliminated on the other. This generates growing demand for adequate QoE estimation models accounting for dominant mobile data services. Moreover, novel models have to fulfill requirements of good applicability and high accuracy. Our approach in this paper details an advanced QoE estimation model, extensively verified with appropriate statistical tools, for the most popular mobile web services: browsing, download, and upload. The proposed model follows from a recent extensive QoE assessment and utilizes the bitrate together with the initial loading delay as well-measurable input parameters. Targeted to estimate the mean opinion score, our proposed model demonstrates an excellent convergence across the considered practical scenarios.

Anglický abstrakt

Intensive competition between network operators as well as steady increase in mobile traffic call for additional investments into the networking infrastructure. Keeping current mobile networks profitable, the following criteria should be satisfied: end-user quality expectations need to be fulfilled on the one hand and service quality overprovisioning should be eliminated on the other. This generates growing demand for adequate QoE estimation models accounting for dominant mobile data services. Moreover, novel models have to fulfill requirements of good applicability and high accuracy. Our approach in this paper details an advanced QoE estimation model, extensively verified with appropriate statistical tools, for the most popular mobile web services: browsing, download, and upload. The proposed model follows from a recent extensive QoE assessment and utilizes the bitrate together with the initial loading delay as well-measurable input parameters. Targeted to estimate the mean opinion score, our proposed model demonstrates an excellent convergence across the considered practical scenarios.

BibTex


@inproceedings{BUT108344,
  author="Jiří {Hošek} and Michal {Ries} and Pavel {Vajsar} and Ľuboš {Nagy} and Sergey {Andreev} and Olga {Galinina} and Yevgeni {Koucheryavy} and Zdeněk {Šulc} and Petr {Hais} and Radek {Penížek}",
  title="Predicting User QoE Satisfaction in Current Mobile Networks",
  annote="Intensive competition between network operators
as well as steady increase in mobile traffic call for additional
investments into the networking infrastructure. Keeping current
mobile networks profitable, the following criteria should be satisfied:
end-user quality expectations need to be fulfilled on the one
hand and service quality overprovisioning should be eliminated
on the other. This generates growing demand for adequate QoE
estimation models accounting for dominant mobile data services.
Moreover, novel models have to fulfill requirements of good
applicability and high accuracy. Our approach in this paper
details an advanced QoE estimation model, extensively verified
with appropriate statistical tools, for the most popular mobile web
services: browsing, download, and upload. The proposed model
follows from a recent extensive QoE assessment and utilizes the
bitrate together with the initial loading delay as well-measurable
input parameters. Targeted to estimate the mean opinion score,
our proposed model demonstrates an excellent convergence across
the considered practical scenarios.",
  address="IEEE",
  booktitle="Proceedings of the IEEE International Conference on Communications (ICC) 2014",
  chapter="108344",
  howpublished="electronic, physical medium",
  institution="IEEE",
  year="2014",
  month="june",
  pages="1094--1099",
  publisher="IEEE",
  type="conference paper"
}