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

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 =6∼7 (-,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 (10,2∼5,1) 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)