Non-intrusive method for video quality prediction over LTE using random neural networks (RNN)

Tarik Mohamed Ghalut, Hadi Larijani

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

    Abstract

    Long Term Evolution (LTE) is the preliminary version of a fourth generation (4G) mobile communication system. Its aim is to support different services with high data rates and strict Quality of Experience (QoE) requirements of users. The main aim of this study is to present a prediction model based on Random Neural Networks (RNNs) for objective, non-intrusive prediction of video quality over LTE for video applications. A three layer feed-forward RNN model with gradient descent training algorithm has been developed. This model uses a combination of objective parameters in the application and network layers, such as Content Type (CT), Sender Bit Rate (SBR), resolution size, Frame Rate (FR), codec, and packet loss rate (PLR). The video quality was predicted in terms of the Mean Opinion Score (MOS). The results show an approximate 50% increase in accuracy using this model, compared to previous models. LTE-Sim software has been used to generate different samples for testing and training RNN model.
    Original languageEnglish
    Title of host publicationProceedings of the 9th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP 14)
    PublisherIEEE
    Pages519-524
    Number of pages6
    DOIs
    Publication statusPublished - 2014

    Keywords

    • long term evolution (LTE)
    • 4G mobile communication
    • random neural networks (RNN)

    Fingerprint Dive into the research topics of 'Non-intrusive method for video quality prediction over LTE using random neural networks (RNN)'. Together they form a unique fingerprint.

  • Cite this

    Ghalut, T. M., & Larijani, H. (2014). Non-intrusive method for video quality prediction over LTE using random neural networks (RNN). In Proceedings of the 9th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP 14) (pp. 519-524). IEEE. https://doi.org/10.1109/CSNDSP.2014.6923884