  Existing reinforcement learning~(RL)-based adaptive bitrate~(ABR) approaches
outperform the previous fixed control rules based methods by improving the
Quality of Experience~(QoE) score, as the QoE metric can hardly provide clear
guidance for optimization, finally resulting in the unexpected strategies. In
this paper, we propose \emph{Tiyuntsong}, a self-play reinforcement learning
approach with generative adversarial network~(GAN)-based method for ABR video
streaming. Tiyuntsong learns strategies automatically by training two agents
who are competing against each other. Note that the competition results are
determined by a set of rules rather than a numerical QoE score that allows
clearer optimization objectives. Meanwhile, we propose GAN Enhancement Module
to extract hidden features from the past status for preserving the information
without the limitations of sequence lengths. Using testbed experiments, we show
that the utilization of GAN significantly improves the Tiyuntsong's
performance. By comparing the performance of ABRs, we observe that Tiyuntsong
also betters existing ABR algorithms in the underlying metrics.
