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

Rajarshi Bhattacharyya; Archana Bura; Desik Rengarajan; Mason Rumuly; Bainan Xia; Srinivas Shakkottai; Dileep Kalathil; Ricky K. P. Mok; Amogh Dhamdhere

arXiv · 1901.00959

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

Rajarshi Bhattacharyya·Archana Bura·Desik Rengarajan·Mason Rumuly·Bainan Xia·Srinivas Shakkottai·Dileep Kalathil·Ricky K. P. Mok·Amogh Dhamdhere

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Abstract

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.

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Rajarshi Bhattacharyya, Archana Bura, Desik Rengarajan, Mason Rumuly, Bainan Xia, Srinivas Shakkottai, Dileep Kalathil, Ricky K. P. Mok, Amogh Dhamdhere. 2020-05-13. QFlow: A Learning Approach to High QoE Video Streaming at the Wireless Edge. https://arxiv.org/abs/1901.00959

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