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Emerging multimedia applications often use a wireless LAN (Wi-Fi) infrastructure to stream content. These Wi-Fi deployments vary vastly in terms of their system configurations. In this paper, we take a step toward characterizing the Quality of Experience (QoE) of volumetric video streaming over an enterprise-grade Wi-Fi network to: (i) understand the impact of Wi-Fi control parameters on user QoE, (ii) analyze the relation between Quality of Service (QoS) metrics of Wi-Fi networks and application QoE, and (iii) compare the QoE of volumetric video streaming to traditional 2D video applications. We find that Wi-Fi configuration parameters such as channel width, radio interface, access category, and priority queues are important for optimizing Wi-Fi networks for streaming immersive videos.
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QFlow: A Learning Approach to High QoE Video Streaming at the Wireless Edge
The predominant use of wireless access networks is for media streaming applications. 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. Exploiting such reconfigurability requires the design of a system that can enable a configuration, measure the impact on the application performance (Quality of Experience), and adaptively select a new configuration. Effectively, this feedback loop is a Markov Decision Process whose parameters are unknown. The goal of this work is to develop QFlow, a platform that instantiates this feedback loop, and instantiate a variety of control policies over it. We use the popular application of video streaming over YouTube as our use case. Our context is priority queueing, with the action space being that of determining which clients should be assigned to each queue at each decision period. We first develop policies based on model-based and model-free reinforcement learning. We then design an auction-based system under which clients place bids for priority service, as well as a more structured index-based policy. Through experiments, we show how these learning-based policies on QFlow are able to select the right clients for prioritization in a high-load scenario to outperform the best known solutions with over 25% improvement in QoE, and a perfect QoE score of 5 over 85% of the time.
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- Award ID(s):
- 1719384
- PAR ID:
- 10296545
- Date Published:
- Journal Name:
- IEEE/ACM Transactions on Networking
- ISSN:
- 1063-6692
- Page Range / eLocation ID:
- 1 to 15
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TNET.2021.3106675
