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Table 20 Summary of ML-based QoS/QoE prediction models for HAS and DASH

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

Ref. ML Technique Application Dataset Features Output Evaluation
  (training) (approach) (availability)    Settings Resultsab
CS2P [432] Supervised: HMM (offline) Throughput prediction for midstream bitrate adaptation in HAS clients to improve the QoE for video streaming (regression) iQIYI dataset consisting of 20 million sessions covering ·3 million unique clients IPs and ·18 server IPs ·87 ISPs Throughput samples Throughput 110 periods ahead HMM model per cluster of similar sessions: · Number of states =6 · Number of samples =100 SVM, GBR single model for all sessions: · Number of features=6 MAE =7%(on normalized data) · up to 50% more accurate than SVR, GBR and HMM with no clustering ·3.2% improvement on overall QoE ·10.9% improved bitrate over MPC
Claeys et al. [102] Reinforcement learning: Q-Learning (online) Video quality adaptation in a HAS client to maximize QoE under varying network conditions (rule extraction) ns-3 simulation based on TCP streaming sessions in Norway’s Telenor 3G/HSDPA mobile wireless network dataset. (public [384]) State: · client buffer filling level · client throughput level Reward: QoE as function of · targeted quality level · span between current and targeted video quality level · rebuffering level Finite action set of N=7 possible video quality levels (sotmax selection) Improvement compared to Microsoft MSS: ·9.12% higher estimated MOS ·16.65% lower standard deviation ·S=(N+1)\(\frac {B_{max}}{T_{seg} +1}\) ·A=N
  1. aAverage values. Results vary according to experimental settings
  2. bEvaluation metrics: mean absolute error (MAE); S: number of state variables; A: number of possible actions per state