LAUSR

Mobile video streaming occupies three-quarters of today's cellular network traffic. The quality of mobile videos becomes increasingly important for video providers to attract more users. For example, they invest in network bandwidth resources and conduct adaptive bitrate techniques to improve video quality. Prior adaptive bitrate (ABR) algorithms perform well under given throughput traces on broadband and WiFi networks. They may perform poorly for mobile video streaming due to the high network dynamics of cellular networks. To study the properties of throughput traces under cellular networks, we collect 4G network throughput traces for over four months in two large cities, Beijing and Suzhou in China. We derive the environment-specific Markov property of throughputs in the dataset. Accordingly, we propose NEIVA, an environment identification based technique to adaptively predict future throughput for different types of environments. We also implement NEIVA and integrate it with the state-of-the-art ABR algorithm, model predictive control (MPC) approach in our testbed for experiments. By emulating mobile video streaming under throughput traces in our dataset, NEIVA achieves 20 - 25 percent improvement on throughput prediction accuracy comparing to baseline predictors. Meanwhile, NEIVA achieves 11 - 20 percent user QoE improvement over MPC with baseline predictors.