Real-time visual communications are popular nowadays. Object assessment of transmitted video quality, which is correlated with the human visual experience, is challenging in two aspects. First, there is no reference video. Second, end terminals have limited computational power. A machine-learning-based solution to blind quality assessment of real-time video to be implemented in end terminals is presented in this talk.