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QoS intelligent prediction for mobile video networks: a GR approach

  • S.I. : SPIoT 2020
  • Published:
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Abstract

With the growth of mobile devices, consumer networks make the life more convenient and faster. Consumer networks consider mobile video as an important communication mode. Mobile video transmission faces complex environments, and the quality of service (QoS) of mobile video networks is very important for mobile entertainment applications. To evaluate the QoS of mobile video networks, outage probability (OP) is an important criterion. However, the mobile video networks gradually become complex, dynamic, and variable, which make it increasingly more difficult to predict the OP performance. In this paper, we investigate the OP performance analysis and prediction. The OP expressions are derived in exact closed-form. Then, based on the characteristics of mobile data, we have established a prediction model based on generalized regression (GR) neural network. A GR-based OP performance intelligent prediction algorithm is proposed. Compared with other methods, our proposed approach can obtain a better prediction effect. The prediction accuracy of the proposed approach can be increased by 64% and 58%, respectively. The running time is also the shortest.

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Acknowledgements

This research was supported by the Shandong Province Natural Science Foundation (No. ZR2017BF023), the National Natural Science Foundation of China (No. 61702295), the Opening Foundation of Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University (No. MJUKF-IPIC202009), the Opening Foundation of Key Laboratory of Opto-Technology and Intelligent Control (Lanzhou Jiaotong University), The Ministry of Education (No. KFKT2020-09), the Shandong Province Colleges and Universities Young Talents Initiation Program (No. 2019KJN047), the Shandong Province Postdoctoral Innovation Project (No. 201703032), and the Doctoral Found of QUST (No. 1203043003480,010029029).

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Authors and Affiliations

  1. College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, 266061, China

    Lingwei Xu & Hui Li

  2. Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University), Fuzhou, 350108, China

    Lingwei Xu & Wenzhong Lin

  3. Key Laboratory of Opto-technology and Intelligent Control, Ministry of Education, Lanzhou Jiaotong University, Lanzhou, 730070, China

    Lingwei Xu

  4. Institute of Data Science, City University of Macau, Macau, 999078, China

    Han Wang

  5. Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, V8W 2Y2, Canada

    T. Aaron Gulliver

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  1. Lingwei Xu
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  2. Han Wang
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  3. Hui Li
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Correspondence to Hui Li.

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Xu, L., Wang, H., Li, H. et al. QoS intelligent prediction for mobile video networks: a GR approach. Neural Comput & Applic 33, 3891–3900 (2021). https://doi.org/10.1007/s00521-020-05441-1

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  • DOI: https://doi.org/10.1007/s00521-020-05441-1

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