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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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This content will become publicly available on March 5, 2026
Maestro: QoE-Aware Dynamic Resource Allocation in Wi-Fi Networks
Wi-Fi is an integral part of today's Internet infrastructure, enabling a diverse range of applications and services. Prior approaches to Wi-Fi resource allocation optimized Quality of Service (QoS) metrics, which often do not accurately reflect the user's Quality of Experience (QoE). To address the gap between QoS and QoE, we introduce Maestro, an adaptive method that formulates the Wi-Fi resource allocation problem as a partially observable Markov decision process (PO-MDP) to maximize the overall system QoE and QoE fairness. Maestro estimates QoE without using any application or client data; instead, it treats them as black boxes and leverages temporal dependencies in network telemetry data. Maestro dynamically adjusts policies to handle different classes of applications and variable network conditions. Additionally, Maestro uses a simulation environment for practical training. We evaluate Maestro in an enterprise-level Wi-Fi testbed with a variety of applications, and find that Maestro achieves up to 25× and 78% improvement in QoE and fairness, respectively, compared to the widely-deployed Wi-Fi Multimedia (WMM) policy. Compared to the state-of-the-art learning approach QFlow, Maestro increases QoE by up to 69%. Unlike QFlow which requires modifications to clients, we demonstrate that Maestro improves QoE of popular over-the-top services with unseen traffic without control over clients or servers.
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- Award ID(s):
- 2212200
- PAR ID:
- 10607921
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Proceedings of the ACM on Networking
- Volume:
- 3
- Issue:
- CoNEXT1
- ISSN:
- 2834-5509
- Page Range / eLocation ID:
- 1 to 24
- Subject(s) / Keyword(s):
- Wi-Fi QoE Access Category Priority Queue PO-MDP LSTM DDQN
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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