  The predominant use of wireless access networks is for media streaming
applications, which are only gaining popularity as ever more devices become
available for this purpose. However, current access networks treat all packets
identically, and lack the agility to determine which clients are most in need
of service at a given time. Software reconfigurability of networking devices
has seen wide adoption, and this in turn implies that agile control policies
can be now instantiated on access networks. The goal of this work is to design,
develop and demonstrate QFlow, a learning approach to create a value chain from
the application on one side, to algorithms operating over reconfigurable
infrastructure on the other, so that applications are able to obtain necessary
resources for optimal performance. Using YouTube video streaming as an example,
we illustrate how QFlow is able to adaptively provide such resources and attain
a high QoE for all clients at a wireless access point.
