  Video transmission services adopt adaptive algorithms to ensure users'
demands. Existing techniques are often optimized and evaluated by a function
that linearly combines several weighted metrics. Nevertheless, we observe that
the given function fails to describe the requirement accurately. Thus, such
proposed methods might eventually violate the original needs. To eliminate this
concern, we propose \emph{Zwei}, a self-play reinforcement learning algorithm
for video transmission tasks. Zwei aims to update the policy by
straightforwardly utilizing the actual requirement. Technically, Zwei samples a
number of trajectories from the same starting point and instantly estimates the
win rate w.r.t the competition outcome. Here the competition result represents
which trajectory is closer to the assigned requirement. Subsequently, Zwei
optimizes the strategy by maximizing the win rate. To build Zwei, we develop
simulation environments, design adequate neural network models, and invent
training methods for dealing with different requirements on various video
transmission scenarios. Trace-driven analysis over two representative tasks
demonstrates that Zwei optimizes itself according to the assigned requirement
faithfully, outperforming the state-of-the-art methods under all considered
scenarios.
