The increased use of video conferencing applications (VCAs) has made it critical to understand and support end-user quality of experience (QoE) by all stakeholders in the VCA ecosystem, especially network operators, who typically do not have direct access to client software. Existing VCA QoE estimation methods use passive measurements of application-level Real-time Transport Protocol (RTP) headers. However, a network operator does not always have access to RTP headers in all cases, particularly when VCAs use custom RTP protocols (e.g., Zoom) or due to system constraints (e.g., legacy measurement systems). Given this challenge, this paper considers the use of more standard features in the network traffic, namely, IP and UDP headers, to provide per-second estimates of key VCA QoE metrics such as frames rate and video resolution. We develop a method that uses machine learning with a combination of flow statistics (e.g., throughput) and features derived based on the mechanisms used by the VCAs to fragment video frames into packets. We evaluate our method for three prevalent VCAs running over WebRTC: Google Meet, Microsoft Teams, and Cisco Webex. Our evaluation consists of 54,696 seconds of VCA data collected from both (1), controlled in-lab network conditions, and (2) real-world networks from 15 households. We show that the ML-based approach yields similar accuracy compared to the RTP-based methods, despite using only IP/UDP data. For instance, we can estimate FPS within 2 FPS for up to 83.05% of one-second intervals in the real-world data, which is only 1.76% lower than using the application-level RTP headers.

Cellular network performance does not cleanly generalize. A variety of factors, such as location, terrain, signal quality and network load, affect the performance of services delivered over LTE networks. As a result, the presence of LTE coverage does not always equate to usable service; coverage can be of poor quality, or it can be congested and difficult to access. Given that reliance on LTE networks for Internet connectivity has exploded, it is critical to understand the quality of experience for applications delivered over these networks in a variety of scenarios. To this end, we develop a robust measurement suite that we use to conduct a unique measurement campaign in tribal, rural, congested urban and uncongested urban regions, representing a variety of under-provisioned, congested, and well-provisioned operational LTE networks run by four major providers. Our analysis confirms that the performance of LTE networks in tribal and rural areas is typically worse than even heavily congested urban networks. More specifically, in the regions that we study, LTE networks in under-provisioned (tribal/rural) areas have $ 9\times$ poorer video streaming quality, $ 10\times$ higher video start-up delay, undergo more than $ 10\times$ the number of resolution switches, and lead to more than $ 2\times$ slower Web browsing experience as compared to urban deployments. We show that throughput and latency are $ 11\times$ and $ 3\times$ worse in tribal and rural locations, despite identical LTE carrier subscription plans.

Video conferencing applications (VCAs) have become a critical Internet application during the COVID-19 pandemic, as users worldwide now rely on them for work, school, and telehealth. It is thus increasingly important to understand the resource requirements of different VCAs and how they perform under different network conditions, including: how do application-layer performance metrics (e.g., resolution or frames per second) vary under different link capacity; how VCAs perform under temporary reductions in available capacity; how they compete with themselves, with each other, and with other applications; and how usage modality (e.g., gallery vs. speaker mode) affects utilization. We study three modern VCAs: Zoom, Google Meet, and Microsoft Teams. Answers to these questions differ substantially depending on VCA. First, the average utilization on an unconstrained link varies between 0.8 Mbps and 1.9 Mbps. Given temporary reduction of capacity, some VCAs can take as long as 50 seconds to recover to steady state. Differences in proprietary congestion control algorithms also result in unfair bandwidth allocations: in constrained bandwidth settings, one Zoom video conference can consume more than 75% of the available bandwidth when competing with another VCA (e.g., Meet, Teams). For some VCAs, client utilization can decrease as the number of participants increases, due to the reduced video resolution of each participant's video stream given a larger number of participants. Finally, one participant's viewing mode (e.g., pinning a speaker) can affect the upstream utilization of other participants.

Understanding end-user video Quality of Experience (QoE) is important for Internet Service Providers (ISPs). Existing work presents mechanisms that use network measurement data to estimate video QoE. Most of these mechanisms assume access to packet-level traces, the most-detailed data available from the network. However, collecting packet-level traces can be challenging at a network-wide scale. Therefore, we ask:"Is it feasible to estimate video QoE with lightweight, readily-available, but coarse-grained network data?" We specifically consider data in the form of Transport Layer Security (TLS) transactions that can be collected using a standard proxy and present a machine learning-based methodology to estimate QoE. Our evaluation with three popular streaming services shows that the estimation accuracy using TLS transactions is high (up to 72%) with up to 85% recall in detecting low QoE (low video quality or high re-buffering) instances. Compared to packet traces, the estimation accuracy (recall) is 7% (9%) lower but has up to 60 times lower computation overhead.

Understanding the user-perceived quality of experience (QoE) of HTTP-based video has become critical for content providers, distributors, and network operators. For network operators, monitoring QoE is challenging due to lack of access to video streaming applications, user devices, or servers. Thus, network operators need to rely on the network traffic to infer key metrics that influence video QoE. Furthermore, with content providers increasingly encrypting the network traffic, the task of QoE inference from passive measurements has become even more challenging. In this paper, we present a methodology called eMIMIC that uses passive network measurements to estimate key video QoE metrics for encrypted HTTP-based adaptive streaming (HAS) sessions. eMIMIC uses packet headers from network traffic to model an HAS session and estimate video QoE metrics, such as average bitrate and re-buffering ratio. We evaluate our methodology using network traces from a variety of realistic conditions and ground truth collected using a lab testbed for video sessions from three popular services, two video on demand (VoD) and one Live. eMIMIC estimates re-buffering ratio within 1% point of ground truth for up to 75% sessions in VoD (80% in Live) and average bitrate with error under 100 Kb/s for up to 80% sessions in VoD (70% in Live). We also compare eMIMIC with recently proposed machine learning-based QoE estimation methodology. We show that eMIMIC can predict average bitrate with 2.8%-3.2% higher accuracy and re-buffering ratio with 9.8%-24.8% higher accuracy without requiring any training on ground truth QoE metrics. Finally, we show that eMIMIC can estimate real-time QoE metrics with at least 89.6% accuracy in identifying buffer occupancy state and at least 85.7% accuracy in identifying average bitrate class of recently downloaded chunks.

Video streaming traffic is rapidly growing in mobile networks. Mobile Network Operators (MNOs) are expected to keep up with this growing demand, while maintaining a high video Quality of Experience (QoE). This makes it critical for MNOs to have a solid understanding of users' video QoE with a goal to help with network planning, provisioning and traffic management. However, designing a system to measure video QoE has several challenges: i) large scale of video traffic data and diversity of video streaming services, ii) cross-layer constraints due to complex cellular network architecture, and iii) extracting QoE metrics from network traffic. In this paper, we present VideoNOC, a prototype of a flexible and scalable platform to infer objective video QoE metrics (e.g., bitrate, rebuffering) for MNOs. We describe the design and architecture of VideoNOC, and outline the methodology to generate a novel data source for fine-grained video QoE monitoring. We then demonstrate some of the use cases of such a monitoring system. VideoNOC reveals video demand across the entire network, provides valuable insights on a number of design choices by content providers (e.g., OS-dependent performance, video player parameters like buffer size, range of encoding bitrates, etc.) and helps analyze the impact of network conditions on video QoE (e.g., mobility and high demand).

Multimedia streaming is a major mobile application, accounting for more than half of total mobile traffic. Streaming applications usually have a static buffering strategy. For example, buffer size is limited to x minutes of the stream, where x is optimized to provide the best trade-off between minimizing stalls and limiting waste of user's bandwidth and energy resulting from user abandonment. We show that such strategies based on information available on the mobile device alone do not work well when network conditions change dynamically, e.g., connectivity degrades due to congestion. We propose an alternative strategy using the framework called TANGO, based on a novel idea of cooperation between cellular network and mobile devices. By monitoring real-time network conditions and continuously predicting user location, our system is able to predict connectivity degradation in the near term. In such events, a notification is sent to the mobile device so that the streaming application can initiate a mitigation action, such as to pre-cache more content. In simulations based on real user traces, we found that TANGO reduces pause time by 13-72%, significantly outperforming DASH, which is the current state of the art.

We investigate the condition on transmission radius needed to achieve connectivity in duty-cycled wireless sensor networks (briefly, DC-WSNs). First, we settle a conjecture of Das et al. [2012] and prove that the connectivity condition on random geometric graphs (RGGs), given by Gupta and Kumar [1989], can be used to derive a weakly sufficient condition to achieve connectivity in DC-WSNs. To find a stronger result, we define a new vertex-based random connection model that is of independent interest. Following a proof technique of Penrose [1991], we prove that when the density of the nodes approaches infinity, then a finite component of size greater than 1 exists with probability 0 in this model. We use this result to obtain an optimal condition on node transmission radius that is both necessary and sufficient to achieve connectivity and is hence optimal . The optimality of such a radius is also tested via simulation for two specific duty-cycle schemes, called the contiguous and the random selection duty-cycle schemes. Finally, we design a minimum-radius duty-cycling scheme that achieves connectivity with a transmission radius arbitrarily close to the one required in random geometric graphs. The overhead in this case is that we have to spend some time computing the schedule.

We investigate the condition on transmission radius needed to achieve connectivity in duty-cycled wireless sensor networks (briefly, DC-WSN). First, we settle a conjecture of Das et. al. (2012) and prove that the connectivity condition on Random Geometric Graphs (RGG), given by Gupta and Kumar (1989), can be used to derive a weak sufficient condition to achieve connectivity in DC-WSN. We also present a stronger result which gives a necessary and sufficient condition for connectivity and is hence optimal. The optimality of such a radius is also tested via simulation for two specific duty-cycle schemes, called the contiguous and the random selection duty-cycle scheme.