Abstract: A method, system and computer program product for predicting a viewer's quality of experience while watching mobile videos potentially afflicted with network induced impairments. The length of a stall on a video at time t is received as a first input to a model. The number of stalls up to the time t is received as a second input to the model. Furthermore, the time since a preceding rebuffering event is received as a third input to the model. Additionally, a reciprocal stall density at time t is received as a fourth input to the model. The hysteresis effect is captured using a machine-learning-based model with an input that is an aggregate of the outputs of the first, second, third and fourth inputs to nonlinear input blocks of the model, where the hysteresis effect represents an effect that a viewer's recent level of satisfaction/dissatisfaction has on their overall viewing experience.

Abstract: A method, system and computer program product for predicting a viewer's quality of experience while watching mobile videos potentially afflicted with network induced impairments. The length of a stall on a video at time t is received as a first input to a model. The number of stalls up to the time t is received as a second input to the model. Furthermore, the time since a preceding rebuffering event is received as a third input to the model. Additionally, a reciprocal stall density at time t is received as a fourth input to the model. The hysteresis effect is captured using a machine-learning-based model with an input that is an aggregate of the outputs of the first, second, third and fourth inputs to nonlinear input blocks of the model, where the hysteresis effect represents an effect that a viewer's recent level of satisfaction/dissatisfaction has on their overall viewing experience.