  Rate control is widely adopted during video streaming to provide both high
video qualities and low latency under various network conditions. However,
despite that many work have been proposed, they fail to tackle one major
problem: previous methods determine a future transmission rate as a single for
value which will be used in an entire time-slot, while real-world network
conditions, unlike lab setup, often suffer from rapid and stochastic changes,
resulting in the failures of predictions.
  In this paper, we propose a delay-constrained rate control approach based on
end-to-end deep learning. The proposed model predicts future bit rate not as a
single value, but as possible bit rate ranges using target delay gradient, with
which the transmission delay is guaranteed. We collect a large scale of
real-world live streaming data to train our model, and as a result, it
automatically learns the correlation between throughput and target delay
gradient. We build a testbed to evaluate our approach. Compared with the
state-of-the-art methods, our approach demonstrates a better performance in
bandwidth utilization. In all considered scenarios, a range based rate control
approach outperforms the one without range by 19% to 35% in average QoE
improvement.
