Estimation of video QoE from MAC parameters in wireless network: A Random Neural Network approach

Indira Paudel; Jeevan Pokhrel; Bachar Wehbi; Ana Cavalli; Badii Jouaber

doi:10.1109/ISCIT.2014.7011868

Estimation of video QoE from MAC parameters in wireless network: A Random Neural Network approach

Indira Paudel
, Jeevan Pokhrel
, Bachar Wehbi
, Ana Cavalli
, Badii Jouaber

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Supporting real time video transmission over wireless networks is very challenging. The video flow characteristics can be vulnerable to the time-varying channel conditions and the unreliability of the MAC (Medium Access Control) can substantially degrade Quality of Experience (QoE) in wireless networks. QoE reflects the satisfaction level of a user towards a particular service or application. It can be a valuable input to service providers and help them to enhance the quality of the overall system. In this work, we present a methodology and a system based on Random Neural Network (RNN) to analyze the impact of different MAC-level parameters on video QoE over IEEE 802.11n wireless networks. At first, subjective tests are performed to correlate MAC-level parameters with user's perceived video QoE. Secondly, we propose a RNN technique to estimate the impact of these parameters on video QoE. The experimental results show that the proposed method can effectively measure the impact of MAC-level parameters on video QoE. The acceptable correlation between subjective QoE and estimated QoE verifies the validity of the obtained results.

Original language	English
Title of host publication	14th International Symposium on Communications and Information Technologies, ISCIT 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	51-55
Number of pages	5
ISBN (Electronic)	9781479944163
DOIs	https://doi.org/10.1109/ISCIT.2014.7011868
Publication status	Published - 15 Jan 2015
Event	14th International Symposium on Communications and Information Technologies, ISCIT 2014 - Incheon, Korea, Republic of Duration: 24 Sept 2014 → 26 Sept 2014

Publication series

Name	14th International Symposium on Communications and Information Technologies, ISCIT 2014

Conference

Conference	14th International Symposium on Communications and Information Technologies, ISCIT 2014
Country/Territory	Korea, Republic of
City	Incheon
Period	24/09/14 → 26/09/14

Keywords

IEEE 802.11n
Medium Access Control
Quality of Experience
Quality of Service
Random Neural Network
Video Traffic
Wireless Networks

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10.1109/ISCIT.2014.7011868

Cite this

Paudel, I., Pokhrel, J., Wehbi, B., Cavalli, A., & Jouaber, B. (2015). Estimation of video QoE from MAC parameters in wireless network: A Random Neural Network approach. In 14th International Symposium on Communications and Information Technologies, ISCIT 2014 (pp. 51-55). Article 7011868 (14th International Symposium on Communications and Information Technologies, ISCIT 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCIT.2014.7011868

Paudel, Indira ; Pokhrel, Jeevan ; Wehbi, Bachar et al. / Estimation of video QoE from MAC parameters in wireless network : A Random Neural Network approach. 14th International Symposium on Communications and Information Technologies, ISCIT 2014. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 51-55 (14th International Symposium on Communications and Information Technologies, ISCIT 2014).

@inproceedings{2957530b8a0b4d28b09e3c5077e452ad,

title = "Estimation of video QoE from MAC parameters in wireless network: A Random Neural Network approach",

abstract = "Supporting real time video transmission over wireless networks is very challenging. The video flow characteristics can be vulnerable to the time-varying channel conditions and the unreliability of the MAC (Medium Access Control) can substantially degrade Quality of Experience (QoE) in wireless networks. QoE reflects the satisfaction level of a user towards a particular service or application. It can be a valuable input to service providers and help them to enhance the quality of the overall system. In this work, we present a methodology and a system based on Random Neural Network (RNN) to analyze the impact of different MAC-level parameters on video QoE over IEEE 802.11n wireless networks. At first, subjective tests are performed to correlate MAC-level parameters with user's perceived video QoE. Secondly, we propose a RNN technique to estimate the impact of these parameters on video QoE. The experimental results show that the proposed method can effectively measure the impact of MAC-level parameters on video QoE. The acceptable correlation between subjective QoE and estimated QoE verifies the validity of the obtained results.",

keywords = "IEEE 802.11n, Medium Access Control, Quality of Experience, Quality of Service, Random Neural Network, Video Traffic, Wireless Networks",

author = "Indira Paudel and Jeevan Pokhrel and Bachar Wehbi and Ana Cavalli and Badii Jouaber",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 14th International Symposium on Communications and Information Technologies, ISCIT 2014 ; Conference date: 24-09-2014 Through 26-09-2014",

year = "2015",

month = jan,

day = "15",

doi = "10.1109/ISCIT.2014.7011868",

language = "English",

series = "14th International Symposium on Communications and Information Technologies, ISCIT 2014",

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Paudel, I, Pokhrel, J, Wehbi, B, Cavalli, A & Jouaber, B 2015, Estimation of video QoE from MAC parameters in wireless network: A Random Neural Network approach. in 14th International Symposium on Communications and Information Technologies, ISCIT 2014., 7011868, 14th International Symposium on Communications and Information Technologies, ISCIT 2014, Institute of Electrical and Electronics Engineers Inc., pp. 51-55, 14th International Symposium on Communications and Information Technologies, ISCIT 2014, Incheon, Korea, Republic of, 24/09/14. https://doi.org/10.1109/ISCIT.2014.7011868

Estimation of video QoE from MAC parameters in wireless network: A Random Neural Network approach. / Paudel, Indira; Pokhrel, Jeevan; Wehbi, Bachar et al.
14th International Symposium on Communications and Information Technologies, ISCIT 2014. Institute of Electrical and Electronics Engineers Inc., 2015. p. 51-55 7011868 (14th International Symposium on Communications and Information Technologies, ISCIT 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T2 - 14th International Symposium on Communications and Information Technologies, ISCIT 2014

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AU - Pokhrel, Jeevan

AU - Wehbi, Bachar

AU - Cavalli, Ana

AU - Jouaber, Badii

PY - 2015/1/15

Y1 - 2015/1/15

N2 - Supporting real time video transmission over wireless networks is very challenging. The video flow characteristics can be vulnerable to the time-varying channel conditions and the unreliability of the MAC (Medium Access Control) can substantially degrade Quality of Experience (QoE) in wireless networks. QoE reflects the satisfaction level of a user towards a particular service or application. It can be a valuable input to service providers and help them to enhance the quality of the overall system. In this work, we present a methodology and a system based on Random Neural Network (RNN) to analyze the impact of different MAC-level parameters on video QoE over IEEE 802.11n wireless networks. At first, subjective tests are performed to correlate MAC-level parameters with user's perceived video QoE. Secondly, we propose a RNN technique to estimate the impact of these parameters on video QoE. The experimental results show that the proposed method can effectively measure the impact of MAC-level parameters on video QoE. The acceptable correlation between subjective QoE and estimated QoE verifies the validity of the obtained results.

AB - Supporting real time video transmission over wireless networks is very challenging. The video flow characteristics can be vulnerable to the time-varying channel conditions and the unreliability of the MAC (Medium Access Control) can substantially degrade Quality of Experience (QoE) in wireless networks. QoE reflects the satisfaction level of a user towards a particular service or application. It can be a valuable input to service providers and help them to enhance the quality of the overall system. In this work, we present a methodology and a system based on Random Neural Network (RNN) to analyze the impact of different MAC-level parameters on video QoE over IEEE 802.11n wireless networks. At first, subjective tests are performed to correlate MAC-level parameters with user's perceived video QoE. Secondly, we propose a RNN technique to estimate the impact of these parameters on video QoE. The experimental results show that the proposed method can effectively measure the impact of MAC-level parameters on video QoE. The acceptable correlation between subjective QoE and estimated QoE verifies the validity of the obtained results.

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KW - Video Traffic

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M3 - Conference contribution

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SP - 51

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BT - 14th International Symposium on Communications and Information Technologies, ISCIT 2014

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 24 September 2014 through 26 September 2014

ER -

Paudel I, Pokhrel J, Wehbi B, Cavalli A, Jouaber B. Estimation of video QoE from MAC parameters in wireless network: A Random Neural Network approach. In 14th International Symposium on Communications and Information Technologies, ISCIT 2014. Institute of Electrical and Electronics Engineers Inc. 2015. p. 51-55. 7011868. (14th International Symposium on Communications and Information Technologies, ISCIT 2014). doi: 10.1109/ISCIT.2014.7011868

Estimation of video QoE from MAC parameters in wireless network: A Random Neural Network approach

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