  Adaptive Bit Rate (ABR) decision plays a crucial role for ensuring
satisfactory Quality of Experience (QoE) in video streaming applications, in
which past network statistics are mainly leveraged for future network bandwidth
prediction. However, most algorithms, either rules-based or learning-driven
approaches, feed throughput traces or classified traces based on traditional
statistics (i.e., mean/standard deviation) to drive ABR decision, leading to
compromised performances in specific scenarios. Given the diverse network
connections (e.g., WiFi, cellular and wired link) from time to time, this paper
thus proposes to learn the ANT (a.k.a., Accurate Network Throughput) model to
characterize the full spectrum of network throughput dynamics in the past for
deriving the proper network condition associated with a specific cluster of
network throughput segments (NTS). Each cluster of NTS is then used to generate
a dedicated ABR model, by which we wish to better capture the network dynamics
for diverse connections. We have integrated the ANT model with existing
reinforcement learning (RL)-based ABR decision engine, where different ABR
models are applied to respond to the accurate network sensing for better rate
decision. Extensive experiment results show that our approach can significantly
improve the user QoE by 65.5% and 31.3% respectively, compared with the
state-of-the-art Pensive and Oboe, across a wide range of network scenarios.
