A Hybrid Machine Learning Approach for Improvised QoE in Video Services over 5G Wireless Networks

作     者：K.B.Ajeyprasaath P.Vetrivelan 

作者机构：School of Electronics EngineeringVellore Institute of TechnologyChennaiTamil Nadu600127India 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2024年第78卷第3期

页      面：3195-3213页

核心收录：

学科分类：12[管理学] 080904[工学-电磁场与微波技术] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0810[工学-信息与通信工程] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 0805[工学-材料科学与工程（可授工学、理学学位）] 0835[工学-软件工程] 081001[工学-通信与信息系统] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：VIT University 

主　　题：Hybrid XGBStackQoE-model machine learning MOS performance metrics QoE 5G video services 

摘      要：Video streaming applications have grown considerably in recent *** a result,this becomes one of the most significant contributors to global internet *** to recent studies,the telecommunications industry loses millions of dollars due to poor video Quality of Experience(QoE)for *** the standard proposals for standardizing the quality of video streaming over internet service providers(ISPs)is the Mean Opinion Score(MOS).However,the accurate finding of QoE by MOS is subjective and laborious,and it varies depending on the user.A fully automated data analytics framework is required to reduce the inter-operator variability characteristic in QoE *** work addresses this concern by suggesting a novel hybrid XGBStackQoE analytical model using a two-level layering *** one combines multiple Machine Learning(ML)models via a layer one Hybrid *** ML models at level one are trained using the entire training data *** level two Hybrid XGBStackQoE-Model is fitted using the outputs(meta-features)of the layer one ML *** proposed model outperformed the conventional models,with an accuracy improvement of 4 to 5 percent,which is still higher than the current traditional *** proposed framework could significantly improve video QoE accuracy.