2019/01/03 by Rajarshi Bhattacharyya, Archana Bura, Bhattacharyya, Rajarshi +15
Computer Science · #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Image and Video Quality Assessment #IoT and Edge/Fog Computing #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Software-Defined Networks and 5G #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1901.00959
openalex publication_date 2019/01/03 · openalex created_date 2022/07/30 · openalex updated_date 2026/07/28
The predominant use of wireless access networks is for media streaming\napplications, which are only gaining popularity as ever more devices become\navailable for this purpose. However, current access networks treat all packets\nidentically, and lack the agility to determine which clients are most in need\nof service at a given time. Software reconfigurability of networking devices\nhas seen wide adoption, and this in turn implies that agile control policies\ncan be now instantiated on access networks. The goal of this work is to design,\ndevelop and demonstrate QFlow, a learning approach to create a value chain from\nthe application on one side, to algorithms operating over reconfigurable\ninfrastructure on the other, so that applications are able to obtain necessary\nresources for optimal performance. Using YouTube video streaming as an example,\nwe illustrate how QFlow is able to adaptively provide such resources and attain\na high QoE for all clients at a wireless access point.\n