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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. 

The increasing popularity of video streaming and conferencing services have altered the nature of Internet traffic. In this paper, we take a first step toward quantifying the impact of this changing nature of traffic on the Quality of Experience (QoE) of popular video streaming and conferencing applications. We first analyze the traffic characteristics of these applications and of backbone links, and show how simple multipath routing may adversely impact application QoE. To mitigate this problem, we propose a new routing path selection approach, inspired by the TCP timeout computation algorithm, that uses both the average and variation of path load. Preliminary results show that this approach improves application QoE by on average 14% and packet latency by 11% for video streaming and conferencing applications, respectively. 

In this work, we examine the challenges that service providers encounter in managing complex service function graphs, while controlling service delivery latency. Based on the lessons we learn, we outline the design of a new system, Invenio, that empowers providers to effectively place microservices without prior knowledge of service functionality. Invenio correlates user actions with the messages they trigger seen in network traces, and computes procedural affinity for communication among microservices for each user action. The procedural affinity values can then be used to make placement decisions to meet latency constraints of individual user actions. Preliminary experiments with the Clearwater IP Multimedia Subsystem demonstrate that even a single high-latency link can result in significant performance degradation, and placement with Invenio can increase user quality of experience. 

We propose CoRE, a 360° video streaming approach that reduces bandwidth requirements compared to transferring the entire 360° video. CoRE uses non-linear sampling in both the spatial and temporal domains to achieve robustness to view direction prediction error and to transient wireless network bandwidth fluctuation. Each CoRE frame samples the environment in all directions, with full resolution over the predicted field of view and gradually decreasing resolution at the periphery, so that missing pixels are avoided, irrespective of the view prediction error magnitude. A CoRE video chunk has a main part at full frame rate, and an extension part at a gradually decreasing frame rate, which avoids stalls while waiting for a delayed transfer. We evaluate a prototype implementation of CoRE through trace-based experiments and a user study, and find that, compared to tiling with low-resolution padding, CoRE reduces data transfer amounts, stalls, and H.264 decoding overhead, increases frame rates, and eliminates missing pixels. 

In this paper, we present RoCC, a robust congestion control approach for datacenter networks based on RDMA. RoCC leverages switch queue size as an input to a PI controller, which computes the fair data rate of flows in the queue, signaling it to the flow sources. The PI parameters are self-tuning to guarantee stability, rapid convergence, and fair and near-optimal throughput in a wide range of congestion scenarios. Our simulation and DPDK implementation results show that RoCC can achieve up to 7× reduction in PFC frames generated under high average load levels, compared to DCQCN. At the same time, RoCC can achieve up to 8× lower tail latency, compared to DCQCN and HPCC. We also find that RoCC does not require PFC. The functional components of RoCC are implementable in P4-based and fixed-function switch ASICs. 

Cellular service carriers often employ reactive strategies to assist customers who experience non-outage related individual service degradation issues (e.g., service performance degradations that do not impact customers at scale and are likely caused by network provisioning issues for individual devices). Customers need to contact customer care to request assistance before these issues are resolved. This paper presents our experience with PACE (ProActive customer CarE), a novel, proactive system that monitors, troubleshoots and resolves individual service issues, without having to rely on customers to first contact customer care for assistance. PACE seeks to improve customer experience and care operation efficiency by automatically detecting individual (non-outage related) service issues, prioritizing repair actions by predicting customers who are likely to contact care to report their issues, and proactively triggering actions to resolve these issues. We develop three machine learning-based prediction models, and implement a fully automated system that integrates these prediction models and takes resolution actions for individual customers.We conduct a large-scale trace-driven evaluation using real-world data collected from a major cellular carrier in the US, and demonstrate that PACE is able to predict customers who are likely to contact care due to non-outage related individual service issues with high accuracy. We further deploy PACE into this cellular carrier network. Our field trial results show that PACE is effective in proactively resolving non-outage related individual customer service issues, improving customer experience, and reducing the need for customers to report their service issues. 

We consider the task of learning a parametric Continuous Time Markov Chain (CTMC) sequence model without examples of sequences, where the training data consists entirely of aggregate steady-state statistics. Making the problem harder, we assume that the states we wish to predict are unobserved in the training data. Specifically, given a parametric model over the transition rates of a CTMC and some known transition rates, we wish to extrapolate its steady state distribution to states that are unobserved. A technical roadblock to learn a CTMC from its steady state has been that the chain rule to compute gradients will not work over the arbitrarily long sequences necessary to reach steady state —from where the aggregate statistics are sampled. To overcome this optimization challenge, we propose ∞-SGD, a principled stochastic gradient descent method that uses randomly-stopped estimators to avoid infinite sums required by the steady state computation, while learning even when only a subset of the CTMC states can be observed. We apply ∞-SGD to a real-world testbed and synthetic experiments showcasing its accuracy, ability to extrapolate the steady state distribution to unobserved states under unobserved conditions (heavy loads, when training under light loads), and succeeding in difficult scenarios where even a tailor-made extension of existing methods fails.