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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. 

Resource flexing is the notion of allocating resources on-demand as workload changes. This is a key advantage of Virtualized Network Functions (VNFs) over their non-virtualized counterparts. However, it is difficult to balance the timeliness and resource efficiency when making resource flexing decisions due to unpredictable workloads and complex VNF processing logic. In this work, we propose an Elastic resource flexing system for Network functions VIrtualization (ENVI) that leverages a combination of VNF-level features and infrastructure-level features to construct a neural-network-based scaling decision engine for generating timely scaling decisions. To adapt to dynamic workloads, we design a window-based rewinding mechanism to update the neural network with emerging workload patterns and make accurate decisions in real time. Our experimental results for real VNFs (IDS Suricata and caching proxy Squid) using workloads generated based on real-world traces, show that ENVI provisions significantly fewer (up to 26%) resources without violating service level objectives, compared to commonly used rule-based scaling policies.