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CS2P [432]
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Supervised: HMM (offline)
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Throughput prediction for midstream bitrate adaptation in HAS clients to improve the QoE for video streaming (regression)
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iQIYI dataset consisting of 20 million sessions covering ·3 million unique clients IPs and ·18 server IPs ·87 ISPs
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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
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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
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Claeys et al. [102]
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Reinforcement learning: Q-Learning (online)
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Video quality adaptation in a HAS client to maximize QoE under varying network conditions (rule extraction)
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ns-3 simulation based on TCP streaming sessions in Norway’s Telenor 3G/HSDPA mobile wireless network dataset. (public [384])
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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
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Finite action set of N=7 possible video quality levels (sotmax selection)
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Improvement compared to Microsoft MSS: ·9.12% higher estimated MOS ·16.65% lower standard deviation
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·S=(N+1)\(\frac {B_{max}}{T_{seg} +1}\) ·A=N
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