Content-based video quality prediction using random neural networks for video streaming over LTE networks

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Quality of experience (QoE) varies dramatically according to the video content. The most common video quality prediction models are based on the application and network layers. However, little research has been done to predict video quality on the basis of video content over fourth-generation (4G) wireless networks. The aim of this study is to classify video content according to the impact of video content on video quality in streaming H.264 video over long-term evolution (LTE) networks, using 'cluster analysis'. This classification is used to develop novel content-based prediction models using random neural networks (RNNs) for video applications. The content based prediction models are then trained with a gradient descent (GD) training algorithm for the four distinct content types and tested using an untrained dataset. Our content-based prediction models are used to establish the relationship between network, application, and LTE related layers to video quality for all video content types. The proposed video prediction model performed well in terms of the root mean squared error (RMSE) and Pearson correlation coefficient an about 12% increase compared to fuzzy logic and adaptive neural fuzzy models. Our simulation results demonstrated that the proposed scheme provides good predictive accuracy (~ 93%) between the measured and predicted values. This work can potentially help in developing accurate and effective reference-free video prediction models and admission quality of service (QoS) control mechanisms for video streaming over LTE networks.
    Original languageEnglish
    Title of host publication2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM)
    Place of PublicationLiverpool
    PublisherIEEE
    Pages1626-1631
    Number of pages6
    ISBN (Electronic)9781509001545
    ISBN (Print)9781509001538
    DOIs
    Publication statusPublished - 28 Dec 2015

    Fingerprint

    Long Term Evolution (LTE)
    Video streaming
    Neural networks
    Network layers
    Cluster analysis
    Fuzzy logic
    Wireless networks
    Quality of service

    Keywords

    • video quality prediction
    • random neural networks
    • network layers
    • streaming media
    • quality assessment
    • video recording
    • quality of service
    • predictive models
    • long term evolution
    • mathematical model
    • LTE
    • QoE
    • QoS
    • RNN

    Cite this

    Ghalut, T., Larijani, H., & Shahrabi, A. (2015). Content-based video quality prediction using random neural networks for video streaming over LTE networks. In 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM) (pp. 1626-1631). Liverpool: IEEE. https://doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.245
    Ghalut, T. ; Larijani, H. ; Shahrabi, A. / Content-based video quality prediction using random neural networks for video streaming over LTE networks. 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM). Liverpool : IEEE, 2015. pp. 1626-1631
    @inproceedings{19bf834de2bc42e185d94dd20c9d53e2,
    title = "Content-based video quality prediction using random neural networks for video streaming over LTE networks",
    abstract = "Quality of experience (QoE) varies dramatically according to the video content. The most common video quality prediction models are based on the application and network layers. However, little research has been done to predict video quality on the basis of video content over fourth-generation (4G) wireless networks. The aim of this study is to classify video content according to the impact of video content on video quality in streaming H.264 video over long-term evolution (LTE) networks, using 'cluster analysis'. This classification is used to develop novel content-based prediction models using random neural networks (RNNs) for video applications. The content based prediction models are then trained with a gradient descent (GD) training algorithm for the four distinct content types and tested using an untrained dataset. Our content-based prediction models are used to establish the relationship between network, application, and LTE related layers to video quality for all video content types. The proposed video prediction model performed well in terms of the root mean squared error (RMSE) and Pearson correlation coefficient an about 12{\%} increase compared to fuzzy logic and adaptive neural fuzzy models. Our simulation results demonstrated that the proposed scheme provides good predictive accuracy (~ 93{\%}) between the measured and predicted values. This work can potentially help in developing accurate and effective reference-free video prediction models and admission quality of service (QoS) control mechanisms for video streaming over LTE networks.",
    keywords = "video quality prediction, random neural networks, network layers, streaming media, quality assessment, video recording, quality of service, predictive models, long term evolution, mathematical model, LTE, QoE, QoS, RNN",
    author = "T. Ghalut and H. Larijani and A. Shahrabi",
    year = "2015",
    month = "12",
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    Ghalut, T, Larijani, H & Shahrabi, A 2015, Content-based video quality prediction using random neural networks for video streaming over LTE networks. in 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM). IEEE, Liverpool, pp. 1626-1631. https://doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.245

    Content-based video quality prediction using random neural networks for video streaming over LTE networks. / Ghalut, T.; Larijani, H.; Shahrabi, A.

    2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM). Liverpool : IEEE, 2015. p. 1626-1631.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

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    N2 - Quality of experience (QoE) varies dramatically according to the video content. The most common video quality prediction models are based on the application and network layers. However, little research has been done to predict video quality on the basis of video content over fourth-generation (4G) wireless networks. The aim of this study is to classify video content according to the impact of video content on video quality in streaming H.264 video over long-term evolution (LTE) networks, using 'cluster analysis'. This classification is used to develop novel content-based prediction models using random neural networks (RNNs) for video applications. The content based prediction models are then trained with a gradient descent (GD) training algorithm for the four distinct content types and tested using an untrained dataset. Our content-based prediction models are used to establish the relationship between network, application, and LTE related layers to video quality for all video content types. The proposed video prediction model performed well in terms of the root mean squared error (RMSE) and Pearson correlation coefficient an about 12% increase compared to fuzzy logic and adaptive neural fuzzy models. Our simulation results demonstrated that the proposed scheme provides good predictive accuracy (~ 93%) between the measured and predicted values. This work can potentially help in developing accurate and effective reference-free video prediction models and admission quality of service (QoS) control mechanisms for video streaming over LTE networks.

    AB - Quality of experience (QoE) varies dramatically according to the video content. The most common video quality prediction models are based on the application and network layers. However, little research has been done to predict video quality on the basis of video content over fourth-generation (4G) wireless networks. The aim of this study is to classify video content according to the impact of video content on video quality in streaming H.264 video over long-term evolution (LTE) networks, using 'cluster analysis'. This classification is used to develop novel content-based prediction models using random neural networks (RNNs) for video applications. The content based prediction models are then trained with a gradient descent (GD) training algorithm for the four distinct content types and tested using an untrained dataset. Our content-based prediction models are used to establish the relationship between network, application, and LTE related layers to video quality for all video content types. The proposed video prediction model performed well in terms of the root mean squared error (RMSE) and Pearson correlation coefficient an about 12% increase compared to fuzzy logic and adaptive neural fuzzy models. Our simulation results demonstrated that the proposed scheme provides good predictive accuracy (~ 93%) between the measured and predicted values. This work can potentially help in developing accurate and effective reference-free video prediction models and admission quality of service (QoS) control mechanisms for video streaming over LTE networks.

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    KW - video recording

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    KW - predictive models

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    KW - mathematical model

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    M3 - Conference contribution

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    Ghalut T, Larijani H, Shahrabi A. Content-based video quality prediction using random neural networks for video streaming over LTE networks. In 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM). Liverpool: IEEE. 2015. p. 1626-1631 https://doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.245