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Adaptive Learning Video Streaming with QoE in multi-home heterogeneous networks

Computer Systems Science & Engineering 2023年第9期46卷 2881-2897页

作者： S.Vijayashaarathi S.NithyaKalyani Department of Electronics and Communication Sona College of TechnologySalem636005Tamil NaduIndia Department of Information Technology K.S.R.College of EngineeringTiruchengode637215Tamil NaduIndia

In recent years,real-time video streaming has grown in *** 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 *** researchers focused on Forward Error Correction(FEC)techniques when attempting to strike a balance between QoE and ***,as network capacity increases,the performance degrades,impacting the live visual ***,Deep Learning(DL)algorithms have been successfully integrated with FEC to stream videos across multiple heterogeneous *** these algorithms need to be changed to make the experience better without sacrificing packet loss and delay *** address the previous challenge,this paper proposes a novel intelligent algorithm that streams video in multi-home heterogeneous networks based on network-centric *** 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 *** 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 *** extensive experimentation,the proposed model outperforms the other existing intelligentmodels 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.

关键词： Real-time video streaming IoT multi-home heterogeneous networks forward error coding deep reinforced gated recurrent networks QoE prediction accuracy RMSE

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