QFlow: A Learning Approach to High QoE Video Streaming at the Wireless Edge
This addresses the issue of inefficient resource allocation in wireless networks for media streaming users, but it is incremental as it builds on existing reconfigurable infrastructure.
The authors tackled the problem of poor video streaming quality in wireless networks by developing QFlow, a learning-based system that dynamically allocates network resources to applications, achieving high Quality of Experience (QoE) for all clients at a wireless access point.
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.