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The predominant use of wireless access networks is for media streamingapplications, which are only gaining popularity as ever more devices becomeavailable for this purpose. However, current access networks treat all packetsidentically, and lack the agility to determine which clients are most in needof service at a given time. Software reconfigurability of networking deviceshas seen wide adoption, and this in turn implies that agile control policiescan 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 fromthe application on one side, to algorithms operating over reconfigurableinfrastructure on the other, so that applications are able to obtain necessaryresources for optimal performance. Using YouTube video streaming as an example,we illustrate how QFlow is able to adaptively provide such resources and attaina high QoE for all clients at a wireless access point.

QFlow: A Learning Approach to High QoE Video Streaming at the Wireless Edge