Table 20 Summary of ML-based QoS/QoE prediction models for HAS and DASH
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 1∼10 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 |