  Media streaming is the dominant application over wireless edge (access)
networks. The increasing softwarization of such networks has led to efforts at
intelligent control, wherein application-specific actions may be dynamically
taken to enhance the user experience. The goal of this work is to develop and
demonstrate learning-based policies for optimal decision making to determine
which clients to dynamically prioritize in a video streaming setting. We
formulate the policy design question as a constrained Markov decision problem
(CMDP), and observe that by using a Lagrangian relaxation we can decompose it
into single-client problems. Further, the optimal policy takes a threshold form
in the video buffer length, which enables us to design an efficient constrained
reinforcement learning (CRL) algorithm to learn it. Specifically, we show that
a natural policy gradient (NPG) based algorithm that is derived using the
structure of our problem converges to the globally optimal policy. We then
develop a simulation environment for training, and a real-world intelligent
controller attached to a WiFi access point for evaluation. We empirically show
that the structured learning approach enables fast learning. Furthermore, such
a structured policy can be easily deployed due to low computational complexity,
leading to policy execution taking only about 15$\mu$s. Using YouTube streaming
experiments in a resource constrained scenario, we demonstrate that the CRL
approach can increase quality of experience (QOE) by over 30\%.
