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Adaptive Learning Video Streaming with QoE in Multi-Home Heterogeneous Networks

S. Vijayashaarathi1,*, S. NithyaKalyani2

1 Department of Electronics and Communication, Sona College of Technology, Salem, 636005, Tamil Nadu, India
2 Department of Information Technology, K. S. R. College of Engineering, Tiruchengode, 637215, Tamil Nadu, India

* Corresponding Author: S. Vijayashaarathi. Email: email

Computer Systems Science and Engineering 2023, 46(3), 2881-2897. https://doi.org/10.32604/csse.2023.036864

Abstract

In recent years, real-time video streaming has grown in popularity. The growing popularity of the Internet of Things (IoT) and other wireless heterogeneous networks mandates that network resources be carefully apportioned among versatile users in order to achieve the best Quality of Experience (QoE) and performance objectives. Most researchers focused on Forward Error Correction (FEC) techniques when attempting to strike a balance between QoE and performance. However, as network capacity increases, the performance degrades, impacting the live visual experience. Recently, Deep Learning (DL) algorithms have been successfully integrated with FEC to stream videos across multiple heterogeneous networks. But these algorithms need to be changed to make the experience better without sacrificing packet loss and delay time. To address the previous challenge, this paper proposes a novel intelligent algorithm that streams video in multi-home heterogeneous networks based on network-centric characteristics. The proposed framework contains modules such as Intelligent Content Extraction Module (ICEM), Channel Status Monitor (CSM), and Adaptive FEC (AFEC). This framework adopts the Cognitive Learning-based Scheduling (CLS) Module, which works on the deep Reinforced Gated Recurrent Networks (RGRN) principle and embeds them along with the FEC to achieve better performances. The complete framework was developed using the Objective Modular Network Testbed in C++ (OMNET++), Internet networking (INET), and Python 3.10, with Keras as the front end and Tensorflow 2.10 as the back end. With extensive experimentation, the proposed model outperforms the other existing intelligent models in terms of improving the QoE, minimizing the End-to-End Delay (EED), and maintaining the highest accuracy (98%) and a lower Root Mean Square Error (RMSE) value of 0.001.

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APA Style
Vijayashaarathi, S., NithyaKalyani, S. (2023). Adaptive learning video streaming with qoe in multi-home heterogeneous networks. Computer Systems Science and Engineering, 46(3), 2881-2897. https://doi.org/10.32604/csse.2023.036864
Vancouver Style
Vijayashaarathi S, NithyaKalyani S. Adaptive learning video streaming with qoe in multi-home heterogeneous networks. Comput Syst Sci Eng. 2023;46(3):2881-2897 https://doi.org/10.32604/csse.2023.036864
IEEE Style
S. Vijayashaarathi and S. NithyaKalyani, "Adaptive Learning Video Streaming with QoE in Multi-Home Heterogeneous Networks," Comput. Syst. Sci. Eng., vol. 46, no. 3, pp. 2881-2897. 2023. https://doi.org/10.32604/csse.2023.036864



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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