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Table 19 Summary of supervised ML-based QoS/QoE correlation models

From: A comprehensive survey on machine learning for networking: evolution, applications and research opportunities

Ref. ML Application Dataset Features Output Evaluation
  Technique (approach) (availability)    Settings Resultsab
Khan et al. [235, 236] ANFIS Impact of network and application-level QoS on MPEG4 video streaming over wireless mobile networks (NR regression) Simulations with Evalvid and ns−2· MPEG4 video source · 3 video types · variable network conditions · mobile video streaming client · PSNR-generated MOS Video type Application-related: frame rate, send bitrate network-level: link bandwidth, packet error rate MOS Number of features =5 5-layer ANFIS-NN: fuzzy layer, product layer, normalized layer, defuzzy layer, total output layer For slight/gentle/rapid motion video type: · RMSE =0.15/0.18/0.56· R 2=0.7/0.8/0.75(on normalized data) Outperformed by a simple regression model [235]
Machado et al. [287] MLP-NN Impact of QoS and video features over QoE (FR/NR regression) Simulations on Evalvid integrated to NS−2·3 video types (slight, gentle, rapid motion) ·565 data points · MOS, PSNR, SSIM and VQM generated by Evalvid and the VQMT tool Delay, jitter, total/I/P/B frame loss · not clear if type of video is considered A model is created for each output · MOS · PSNR · SSIM · VQM Number of features =67 (-,10,1) MOS-MLP (-,10,1) PSN-MLP (-,12,24,1) SSIM-MLP (-,10,1) VQM-MLP MOS-MLP · MSE ≈0.01 PSNR-MLP · MSE ≈0.14 SSIM-MLP · MSE ≈0.01 VQM-MLPMSE · MSE ≈0.3(on normalized data)
Mushtaq et al. [328] DT, RF, NB, SVM, k-NN, and NN Impact of QoS, video features and viewer features over QoE (NR classification) Collected from streaming videos over QoS-controlled emulated network, and MOS collected from a panel of viewers network-level: · delay, jitter, packet loss, etc. application-related: · resolution type of video: · motion complexity viewer-related: · gender, interest, etc. MOS Number of features =9k-NN (k=4) Other settings (N/A) RF · MAE =0.136· TP =74.8% DT · MAE =0.126· TP =74% NB · MAE ≈0.23· TP ≈57% SVM · MAE =0.26· TP ≈61% 4-NN · MAE ≈0.2· TP =49% NN · MAE ≈0.18· TP ≈65%(on normalized data)
MLQoE [89] SVR, MLP-NN, DT, and GNB modular user-centric correlation of QoE and network QoS metrics for VoIP services (NR regression) 3 datasets of VoIP sessions under different network conditions generated with OMNET++: during handover (dataset 1), in a network with heavy UDP traffic (dataset 2), in a network with heavy TCP traffic (dataset 3) QoE assessed with user-generated MOS and program-generated PESQ and E-model QoE network-related: · delay, jitter, packet loss, etc. MOS Number of features =10 MLP-NN (10,25,1) Gaussian, linear, and polynomial kernel SVR SVR · MAE 1=0.66· MAE 2=0.65· MAE 3=0.47 MLP-NN · MAE 1=0.75· MAE 2=0.68· MAE 3=0.53 DT · MAE 1=0.73· MAE 2=0.55· MAE 3=0.5 GNB · MAE 1=0.69· MAE 2=0.68· MAE 3=0.53(on normalized data)
Dermibilek et al. [114] RF, BG, and DNN Correlation of QoE and network and application QoS metrics for video streaming services (NR regression) INRS dataset, including user-generated MOS on audiovisual sequences encoded and transmitted with varying video and network parameters, and other pub (public [112]) network-related: delay, jitter, packet loss, etc. application-related: video frame rates, quantization parameters, filters, etc. MOS Number of features: ·RF1, BG1 =34 ·RF2, BG2 =5 ·DNN21, DNN22 =5 RF, BG tree size =200 Number of hidden layers: ·DNN21=1 · DNN22 hidden =20 RF1 · RMSE =0.340· PCC =0.930 RF2 · RMSE =0.340· PCC =0.930BG1 · RMSE =0.345· PCC =0.928BG2 · RMSE =0.355· PCC =0.925DNN21 · RMSE =0.403· PCC =0.909DNN22 · RMSE =0.437· PCC =0.894(on normalized data)
  1. aAverage values. Results vary according to experimental settings
  2. bAccuracy metrics: mean square error (MSE), mean absolute error (MAE), root MSE (RMSE), correlation coefficient (R), Pearson correlation coefficient (PCC)