Learning-based Framework for QoE Prediction 1.Objective video quality scores (VQA) Use pooling strategy to collapse per-frame objective quality measurements(i.e.SSIM) 2.Rebuffering-aware features(R1 and R2) Use the length of each rebuffering event measured in seconds(R1)and the number of rebuffering events(R2). 3.Memory-related feature (M) The time since the last rebuffering event or rate drop took place and was completed 4.Impairment duration feature (D)Learning-based Framework for QoE Prediction 1.Objective video quality scores (VQA) Use pooling strategy to collapse per-frame objective quality measurements (i.e. SSIM) 2.Rebuffering-aware features (R1 and R2) Use the length of each rebuffering event measured in seconds (R1) and the number of rebuffering events (R2). 3. Memory-related feature (M) The time since the last rebuffering event or rate drop took place and was completed 4. Impairment duration feature (I)