We introduce DeepCrypt, a deep-learning based approach to analyze YouTube adaptive video streaming Quality of Experience (QoE) from the Internet Service Provider (ISP) perspective, relying exclusively on the analysis of encrypted network traffic. Using raw features derived on-line from the encrypted stream of bytes, DeepCrypt infers six different video QoE indicators capturing the user-perceived performance of the service, including the initial playback delay, the number and frequency of rebuffering events, the video playback quality and encoding bitrate, and the number of quality changes. DeepCrypt offers deep visibility into the behavior of the end-user, enabling the fingerprinting and detection of different user actions on the video player, such as video pauses and playback scrubbing (forward, backward, out-of-buffer), offering a complete visibility on the video streaming process from in-network traffic measurements. Evaluations over a large and heterogeneous dataset composed of mobile and fixed-line measurements, using the YouTube HTML5 player, the native YouTube mobile app, as well as a generic HTML5 video player built on top of open source libraries, and considering measurements collected at different ISPs, confirm the out-performance of DeepCrypt over previously used shallow-learning models, and its generalization to different video players and network setups.

Large-scale events pose severe challenges to live video streaming service providers, who need to cope with high, peaking viewer numbers and the resulting fluctuating resource demands, keeping high levels of Quality of Experience (QoE) to avoid end-user frustration and churn. In this paper, we analyze a unique dataset consisting of more than a million 2018 FIFA World Cup mobile live streaming sessions, collected at a large national public broadcaster. Different from previous work, we analyze QoE and user engagement as well as their interaction, in dependency to specific soccer match events, which have the potential to trigger flash crowds during a match. Flash crowds are a particular challenge to video service providers, since they cause sudden load peaks and consequently, the likelihood of quality problems. We further exploit the data to model viewer engagement over the course of a soccer match, and show that client counts follow very similar patterns of change across all matches. We believe that the analysis as well as the resulting models are valuable sources of insight for service providers, equipping them with tools for customer-centric resource and capacity management.

Over the last years, QUIC (Quick UDP Internet Connections) has become the default protocol for networked communication of Google services, heralded as improved successor of the prevailing Transport Control Protocol (TCP). While the deployment of QUIC is increasing, QUIC is also planned to be the foundation of HTTP/3, the next generation of the HTTP protocols, which drive almost all applications on the Web. Given these developments, this paper aims to raise the awareness of the QoE research community to the increasing presence of QUIC, which likely brings implications for QoE monitoring and management of networked multimedia applications, as well as for the overall QoE research agenda. In particular, a major promise during the introduction of QUIC has been the improvement of the QoE of web-based applications (like browsing and video) by overcoming certain limitations and inefficiencies of TCP. In order to validate this claim, a measurement study was conducted to test whether the promised QoE benefits of QUIC are indeed noticeable for end users of streaming and browsing services. Surprisingly, no evidence for any QoE improvement of QUIC over TCP could be found. This way this paper aims to demonstrate how QoE research can and should successfully address relevant current and future developments on the Internet.

Monitoring the Quality of Experience (QoE) undergone by cellular network customers has become paramount for cellular ISPs, who need to ensure high quality levels to limit customer churn due to quality dissatisfaction. This paper tackles the problem of QoE monitoring, assessment and prediction in cellular networks, relying on end-user device (i.e., smart-phone) QoS passive traffic measurements and QoE crowdsourced feedback. We conceive different QoE assessment models based on supervised machine learning techniques, which are capable to predict the QoE experienced by the end user of popular smartphone apps (e.g., YouTube and Facebook), using as input the passive in-device measurements. Using a rich QoE dataset derived from field trials in operational cellular networks, we benchmark the performance of multiple machine learning based predictors, and construct a decision-tree based model which is capable to predict the per-user overall experience and service acceptability with a success rate of 91% and 98% respectively To the best of our knowledge, this is the first paper using end-user, in-device passive measurements and machine learning models to predict the QoE of smartphone users in operational cellular networks.

Currently, we witness dramatically increasing interest in immersive media technologies like Virtual Reality (VR), particularly in omnidirectional video (OV) streaming. Omnidirectional (also called 360-degree) videos are panoramic spherical videos in which the user can look around during playback and which therefore can be understood as hybrids between traditional movie streaming and interactive VR worlds. Unfortunately, streaming this kind of content is extremely bandwidth intensive (compared to traditional 2D video) and therefore, Quality of Experience (QoE) tends to deteriorate significantly in absence of continuous optimal bandwidth conditions. In this paper, we present a first approach towards subjective QoE assessment for omnidirectional video (OV) streaming. We present the results of a lab study on the QoE impact of stalling in the context of OV streaming using head-mounted displays (HMDs). Our findings show that subjective testing for immersive media like OV is not trivial, with even simple cases like stalling leading to unexpected results. After a discussion of characteristic pitfalls and lessons learned, we provide a a set of recommendations for upcoming OV assessment studies.

A quarter of the world population will be using smartphones to access the Internet in the near future. In this context, understanding the quality of experience (QoE) of popular apps in such devices becomes paramount to cellular network operators, who need to offer high-quality levels to reduce the risks of customers churning for quality dissatisfaction. In this paper, we address the problem of QoE provisioning in smartphones from a double perspective, combining the results obtained from subjective laboratory tests with end-device passive measurements and QoE crowd-sourced feedback obtained in operational cellular networks. The study addresses the impact of both access bandwidth and latency on the QoE of five different services and mobile apps: YouTube, Facebook, Web browsing through Chrome, Google Maps, and WhatsApp. We evaluate the influence of both constant and dynamically changing network access conditions, tackling in particular the case of fluctuating downlink bandwidth, which is typical in cellular networks. As a main contribution, we show that the results obtained in the laboratory are highly applicable in the live scenario, as mappings track the QoE provided by users in real networks. We additionally provide hints and bandwidth thresholds for good QoE levels on such apps, as well as discussion on end-device passive measurements and analysis. The results presented in this paper provide a sound basis to better understand the QoE requirements of popular mobile apps, as well as for monitoring the underlying provisioning network. To the best of our knowledge, this is the first paper providing such a comprehensive analysis of QoE in mobile devices, combining network measurements with users QoE feedback in laboratory tests, and operational networks.

A quarter of the world population will be using smartphones to access the Internet in the near future. In this context, understanding the Quality of Experience (QoE) of popular apps in such devices becomes paramount to cellular network operators, who need to offer high quality levels to reduce the risks of customers churning for quality dissatisfaction. In this paper we address the problem of QoE provisioning in smartphones from a double perspective, combining the results obtained from subjective lab tests with end-device passive measurements and QoE crowd-sourced feedback obtained in operational cellular networks. The study addresses the impact of the downlink bandwidth on the QoE of three popular smartphone apps: YouTube, Facebook and Google Maps. As a main contribution, we show that the results obtained in the lab are highly applicable in the live scenario, as mappings track the QoE provided by users in real networks. We additionally provide hints and bandwidth thresholds for good QoE levels on such apps, as well as discussion on end-device passive measurements and analysis. The results presented in this paper provide a sound basis to better understand the QoE requirements of popular mobile apps, as well as for monitoring the underlying provisioning network. To the best of our knowledge, this is the first paper providing such a comprehensive analysis of QoE in mobile devices, combining network measurements with users QoE feedback in lab tests and operational networks.

Since its introduction a few years ago, the concept of `Crowdsourcing' has been heralded as highly attractive alternative approach towards evaluating the Quality of Experience (QoE) of networked multimedia services. The main reason is that, in comparison to traditional laboratory-based subjective quality testing, crowd-based QoE assessment over the Internet promises to be not only much more cost-effective (no lab facilities required, less cost per subject) but also much faster in terms of shorter campaign setup and turnaround times. However, the reliability of remote test subjects and consequently, the trustworthiness of study results is still an issue that prevents the widespread adoption of crowd-based QoE testing. Various ideas for improving user rating reliability and test efficiency have been proposed, with the majority of them relying on a posteriori analysis of results. However, such methods introduce a major lag that significantly affects efficiency of campaign execution. In this paper we address these shortcomings by introducing in momento methods for crowdsourced video QoE assessment which yield improvements of results reliability by factor two and campaign execution efficiency by factor ten. The proposed in momento methods are applicable to existing crowd-based QoE testing approaches and suitable for a variety of service scenarios.

The popularity of the crowdsourcing for performing various tasks online increased significantly in the past few years. The low cost and flexibility of crowdsourcing, in particular, attracted researchers in the field of subjective multimedia evaluations and Quality of Experience (QoE). Since online assessment of multimedia content is challenging, several dedicated frameworks were created to aid in the designing of the tests, including the support of the testing methodologies like ACR, DCR, and PC, setting up the tasks, training sessions, screening of the subjects, and storage of the resulted data. In this paper, we focus on the web-based frameworks for multimedia quality assessments that support commonly used crowdsourcing platforms such as Amazon Mechanical Turk and Microworkers. We provide a detailed overview of the crowdsourcing frameworks and evaluate them to aid researchers in the field of QoE assessment in the selection of frameworks and crowdsourcing platforms that are adequate for their experiments.

Quality of Experience (QoE) in multimedia applications is closely linked to the end users' perception and therefore its assessment requires subjective user studies in order to evaluate the degree of delight or annoyance as experienced by the users. QoE crowdtesting refers to QoE assessment using crowdsourcing, where anonymous test subjects conduct subjective tests remotely in their preferred environment. The advantages of QoE crowdtesting lie not only in the reduced time and costs for the tests, but also in a large and diverse panel of international, geographically distributed users in realistic user settings. However, conceptual and technical challenges emerge due to the remote test settings. Key issues arising from QoE crowdtesting include the reliability of user ratings, the influence of incentives, payment schemes and the unknown environmental context of the tests on the results. In order to counter these issues, strategies and methods need to be developed, included in the test design, and also implemented in the actual test campaign, while statistical methods are required to identify reliable user ratings and to ensure high data quality. This contribution therefore provides a collection of best practices addressing these issues based on our experience gained in a large set of conducted QoE crowdtesting studies. The focus of this article is in particular on the issue of reliability and we use video quality assessment as an example for the proposed best practices, showing that our recommended two-stage QoE crowdtesting design leads to more reliable results.

A novel methodology for QoE evaluation in the social network environment is proposed. It provides high applicability for subjective testing of the multimedia services with respect to real usage scenarios. The environment of social networks provides also significant demographic data and ability to contact extremely many test subjects while allows to focus on or filter specific social groups. QoE results for HD internet video services are presented and followed by discussion on their statistical significance.

Provisioning of mobile video services is rather challenging since in mobile environments, bandwidth and processing resources are limited. Audiovisual content is present in most multimedia services, however, the user expectation of perceived audiovisual quality differs for speech and non-speech contents. The majority of recently proposed metrics for audiovisual quality estimation assumes only one continuous medium, either audio or video. In order to accurately predict the audiovisual quality of a multi-media system it is necessary to apply a metric that takes simultaneously into account audio as well as video quality. When assessing a multi-modal system, one cannot model it only as a simple combination of mono-modal models, because the pure combination of audio and video models does not give a robust perceived-quality performance metric. We show the importance taking into account the cross-modal interaction between audio and video modes also code mutual compensation effect. In this contribution we report on measuring the cross-modal interaction and propose a content adaptive audiovisual metric for video sequences that distinguishes between speech and non-speech audio. Furthermore, the proposed method allows for a reference-free audiovisual quality estimation, which reduces computational complexity and extends applicability.