2prong: Adaptive Video Streaming with DNN and MPC

Adaptive bitrate (ABR) algorithms are often used to optimize the quality of user experience (QoE) during video playback. In the client-side video player, the buffer size and predicted throughput are mainly used to improve user's QoE. However, due to the randomness of mobile network traffic and the heavy-tail effect of the network, it is very difficult to predict throughput. We innovatively use Bayesian neural network to dynamically evaluate video signals. Unlike previous neural network solutions, we use probability distributions instead of point estimates to predict throughput, which can effectively evaluate QoE metrics. Our contributions are to first (i) use of Bayesian neural network to guide video adaptive bitrate adaptation, and then (ii) propose a bitrate adaptive algorithm denoted 2prong, which utilizes high-dimensional contextual information such as buffer occupancy, predicted throughput and video quality to find the most valuable information for quality adaption in real time. We demonstrate the effectiveness of the 2prong algorithm using a simulation testbed. By comparing with other methods, it is demonstrated that 2prong can improve the video quality of video streaming transmission.