Quality of experience and HTTP adaptive streaming: a review of subjective studies

HTTP adaptive streaming technology has become widely spread in multimedia services because of its ability to provide adaptation to characteristics of various viewing devices and dynamic network conditions. There are various studies targeting the optimization of adaptation strategy. However, in order to provide an optimal viewing experience to the end-user, it is crucial to get knowledge about the Quality of Experience (QoE) of different adaptation schemes. This paper overviews the state of the art concerning subjective evaluation of adaptive streaming QoE and highlights the challenges and open research questions related to QoE assessment

Identifying and diagnosing video streaming performance issues

On-line video streaming is an ever evolving ecosystem of services and technologies, where content providers are on a constant race to satisfy the users' demand for richer content and higher bitrate streams, updated set of features and cross-platform compatibility. At the same time, network operators are required to ensure that the requested video streams are delivered through the network with a satisfactory quality in accordance with the existing Service Level Agreements (SLA). However, tracking and maintaining satisfactory video Quality of Experience (QoE) has become a greater challenge for operators than ever before. With the growing popularity of content engagement on handheld devices and over wireless connections, new points-of-failure have added to the list of failures that can affect the video quality. Moreover, the adoption of end-to-end encryption by major streaming services has rendered previously used QoE diagnosis methods obsolete. In this thesis, we identify the current challenges in identifying and diagnosing video streaming issues and we propose novel approaches in order to address them. More specifically, the thesis initially presents methods and tools to identify a wide array of QoE problems and the severity with which they affect the users' experience. The next part of the thesis deals with the investigation of methods to locate under-performing parts of the network that lead to drop of the delivered quality of a service. In this context, we propose a data-driven methodology for detecting the under performing areas of cellular network with sub-optimal Quality of Service (QoS) and video QoE. Moreover, we develop and evaluate a multi-vantage point framework that is capable of diagnosing the underlying faults that cause the disruption of the user's experience. The last part of this work, further explores the detection of network performance anomalies and introduces a novel method for detecting such issues using contextual information. This approach provides higher accuracy when detecting network faults in the presence of high variation and can benefit providers to perform early detection of anomalies before they result in QoE issues.La distribución de vídeo online es un ecosistema de servicios y tecnologías, donde los proveedores de contenidos se encuentran en una carrera continua para satisfacer las demandas crecientes de los usuarios de más riqueza de contenido, velocidad de transmisión, funcionalidad y compatibilidad entre diferentes plataformas. Asimismo, los operadores de red deben asegurar que los contenidos demandados son entregados a través de la red con una calidad satisfactoria según los acuerdos existentes de nivel de servicio (en inglés Service Level Agreement o SLA). Sin embargo, la monitorización y el mantenimiento de un nivel satisfactorio de la calidad de experiencia (en inglés Quality of Experience o QoE) del vídeo online se ha convertido en un reto mayor que nunca para los operadores. Dada la creciente popularidad del consumo de contenido con dispositivos móviles y a través de redes inalámbricas, han aparecido nuevos puntos de fallo que se han añadido a la lista de problemas que pueden afectar a la calidad del vídeo transmitido. Adicionalmente, la adopción de sistemas de encriptación extremo a extremo, por parte de los servicios más importantes de distribución de vídeo online, ha dejado obsoletos los métodos existentes de diagnóstico de la QoE. En esta tesis se identifican los retos actuales en la identificación y diagnóstico de los problemas de transmisión de vídeo online, y se proponen nuevas soluciones para abordar estos problemas. Más concretamente, inicialmente la tesis presenta métodos y herramientas para identificar un conjunto amplio de problemas de QoE y la severidad con los que estos afectan a la experiencia de los usuarios. La siguiente parte de la tesis investiga métodos para localizar partes de la red con un rendimiento bajo que resultan en una disminución de la calidad del servicio ofrecido. En este contexto, se propone una metodología basada en el análisis de datos para detectar áreas de la red móvil que ofrecen un nivel subóptimo de calidad de servicio (en inglés Quality of Service o QoS) y QoE. Además, se desarrolla y se evalúa una solución basada en múltiples puntos de medida que es capaz de diagnosticar los problemas subyacentes que causan la alteración de la experiencia de usuario. La última parte de este trabajo explora adicionalmente la detección de anomalías de rendimiento de la red y presenta un nuevo método para detectar estas situaciones utilizando información contextual. Este enfoque proporciona una mayor precisión en la detección de fallos de la red en presencia de alta variabilidad y puede ayudar a los proveedores a la detección precoz de anomalías antes de que se conviertan en problemas de QoE.La distribució de vídeo online és un ecosistema de serveis i tecnologies, on els proveïdors de continguts es troben en una cursa continua per satisfer les demandes creixents del usuaris de més riquesa de contingut, velocitat de transmissió, funcionalitat i compatibilitat entre diferents plataformes. A la vegada, els operadors de xarxa han d’assegurar que els continguts demandats són entregats a través de la xarxa amb una qualitat satisfactòria segons els acords existents de nivell de servei (en anglès Service Level Agreement o SLA). Tanmateix, el monitoratge i el manteniment d’un nivell satisfactori de la qualitat d’experiència (en anglès Quality of Experience o QoE) del vídeo online ha esdevingut un repte més gran que mai per als operadors. Donada la creixent popularitat del consum de contingut amb dispositius mòbils i a través de xarxes sense fils, han aparegut nous punts de fallada que s’han afegit a la llista de problemes que poden afectar a la qualitat del vídeo transmès. Addicionalment, l’adopció de sistemes d’encriptació extrem a extrem, per part dels serveis més importants de distribució de vídeo online, ha deixat obsolets els mètodes existents de diagnòstic de la QoE. En aquesta tesi s’identifiquen els reptes actuals en la identificació i diagnòstic dels problemes de transmissió de vídeo online, i es proposen noves solucions per abordar aquests problemes. Més concretament, inicialment la tesi presenta mètodes i eines per identificar un conjunt ampli de problemes de QoE i la severitat amb la que aquests afecten a la experiència dels usuaris. La següent part de la tesi investiga mètodes per localitzar parts de la xarxa amb un rendiment baix que resulten en una disminució de la qualitat del servei ofert. En aquest context es proposa una metodologia basada en l’anàlisi de dades per detectar àrees de la xarxa mòbil que ofereixen un nivell subòptim de qualitat de servei (en anglès Quality of Service o QoS) i QoE. A més, es desenvolupa i s’avalua una solució basada en múltiples punts de mesura que és capaç de diagnosticar els problemes subjacents que causen l’alteració de l’experiència d’usuari. L’última part d’aquest treball explora addicionalment la detecció d’anomalies de rendiment de la xarxa i presenta un nou mètode per detectar aquestes situacions utilitzant informació contextual. Aquest enfoc proporciona una major precisió en la detecció de fallades de la xarxa en presencia d’alta variabilitat i pot ajudar als proveïdors a la detecció precoç d’anomalies abans de que es converteixin en problemes de QoE.Postprint (published version

The quality of experience of emerging display technologies

As new display technologies emerge and become part of everyday life, the understanding of the visual experience they provide becomes more relevant. The cognition of perception is the most vital component of visual experience; however, it is not the only cognition that contributes to the complex overall experience of the end-user. Expectations can create significant cognitive bias that may even override what the user genuinely perceives. Even if a visualization technology is somewhat novel, expectations can be fuelled by prior experiences gained from using similar displays and, more importantly, even a single word or an acronym may induce serious preconceptions, especially if such word suggests excellence in quality. In this interdisciplinary Ph.D. thesis, the effect of minimal, one-word labels on the Quality of Experience (QoE) is investigated in a series of subjective tests. In the studies carried out on an ultra-high-definition (UHD) display, UHD video contents were directly compared to their HD counterparts, with and without labels explicitly informing the test participants about the resolution of each stimulus. The experiments on High Dynamic Range (HDR) visualization addressed the effect of the word “premium” on the quality aspects of HDR video, and also how this may affect the perceived duration of stalling events. In order to support the findings, additional tests were carried out comparing the stalling detection thresholds of HDR video with conventional Low Dynamic Range (LDR) video. The third emerging technology addressed by this thesis is light field visualization. Due to its novel nature and the lack of comprehensive, exhaustive research on the QoE of light field displays and content parameters at the time of this thesis, instead of investigating the labeling effect, four phases of subjective studies were performed on light field QoE. The first phases started with fundamental research, and the experiments progressed towards the concept and evaluation of the dynamic adaptive streaming of light field video, introduced in the final phase

Perceptual quality assessment of real-world images and videos

The development of online social-media venues and rapid advances in technology by camera and mobile device manufacturers have led to the creation and consumption of a seemingly limitless supply of visual content. However, a vast majority of these digital images and videos are often afflicted with annoying artifacts during acquisition, subsequent storage, and transmission over the network. All these factors impact the quality of the visual media as perceived by a human observer, thereby compromising their quality of experience (QoE). This dissertation focuses on constructing datasets that are representative of real-world image and video distortions as well as on designing algorithms that accurately predict the perceptual quality of images and videos. The primary goal of this research is to design and demonstrate automatic image and continuous-time video quality predictors that can effectively tackle the widely diverse authentic spatial, temporal, and network-induced distortions -- contrary to all present-day algorithms that operate on single, synthetic visual distortions and predict a single overall quality score for a given video. I introduce an image quality database which contains a large number of images captured using a representative variety of modern mobile devices and afflicted with a widely diverse authentic image distortions. I will also describe the design of an online crowdsourcing system which aided a very large-scale image quality assessment subjective study. This data collection facilitated the design of a new image quality predictor that is founded on the principles of natural scene statistics of images in different color spaces and transform domains. This new quality method is capable of assessing the quality of images with complex mixtures of distortions and yields high correlation with human perception. Pertaining to videos, this dissertation describes a video quality database created to understand the impact of network-induced distortions on an end user's quality of experience. I present the details of a large-scale subjective study that I conducted to gather continuous-time ground truth QoE scores on a collection of 180 videos afflicted with diverse stalling events. I also present my analysis of the temporal variations in the perceived QoE due to the time-varying video quality and present insights on the impact of relevant human cognitive aspects such as long-term and short-term memory and recency on quality perception. Next, I present a continuous-time objective QoE predicting model that effectively captures the complex interactions between the aforementioned human cognitive elements, spatial and temporal distortions, properties of stalling events, and models the state of any given client-side network buffer. I also show how the proposed framework can be extended by further supplementing with any number of additional inputs (or by eliminating any ineffective ones), based on the information available at the content providers during the design of adaptive stream-switching algorithms. This QoE predictor supports future research in the design of quality-aware stream-switching algorithms which could control the position, location, and length of stalls, given a network bandwidth budget and the end user's device information, such that the end user's QoE is maximized.Computer Science

A Measurement Study of Live 360 Video Streaming Systems

360-degree live video streaming is becoming increasingly popular. While providing viewers with enriched experience, 360-degree live video streaming is challenging to achieve since it requires a significantly higher bandwidth and a powerful computation infrastructure. A deeper understanding of this emerging system would benefit both viewers and system designers. Although prior works have extensively studied regular video streaming and 360-degree video on demand streaming, we for the first time investigate the performance of 360-degree live video streaming. We conduct a systematic measurement of YouTube’s 360-degree live video streaming using various metrics in multiple practical settings. Our research insight will help to build a clear understanding of today’s 360-degree live video streaming and lay a foundation for future research on this emerging yet relatively unexplored area. To further understand the delay measured in YouTube’s 360-degree live video streaming, we conduct the second measurement study on a 360-degree live video streaming platform. While live 360-degree video streaming provides an enriched viewing experience, it is challenging to guarantee the user experience against the negative effects introduced by start-up delay, event-to-eye delay, and low frame rate. It is therefore imperative to understand how different computing tasks of a live 360-degree streaming system contribute to these three delay metrics. Our measurement provide insights for future research directions towards improving the user experience of live 360-degree video streaming. Based on our measurement results, we propose a motion-based trajectory transmission method for 360-degree video streaming. First, we design a testbed for 360-degree video playback. The testbed can collect the users viewing data in real time. Then we analyze the trajectories of the moving targets in the 360-degree videos. Specifically, we utilize optical flow algorithms and gaussian mixture model to pinpoint the trajectories. Then we choose the trajectories to be delivered based on the size of the moving targets. The experiment results indicates that our method can obviously reduce the bandwidth consumption

AVQBits-adaptive video quality model based on bitstream information for various video applications

The paper presents AVQBits, a versatile, bitstream-based video quality model. It can be applied in several contexts such as video service monitoring, evaluation of video encoding quality, of gaming video QoE, and even of omnidirectional video quality. In the paper, it is shown that AVQBits predictions closely match video quality ratings obained in various subjective tests with human viewers, for videos up to 4K-UHD resolution (Ultra-High Definition, 3840 x 2180 pixels) and framerates up 120 fps. With the different variants of AVQBits presented in the paper, video quality can be monitored either at the client side, in the network or directly after encoding. The no-reference AVQBits model was developed for different video services and types of input data, reflecting the increasing popularity of Video-on-Demand services and widespread use of HTTP-based adaptive streaming. At its core, AVQBits encompasses the standardized ITU-T P.1204.3 model, with further model instances that can either have restricted or extended input information, depending on the application context. Four different instances of AVQBits are presented, that is, a Mode 3 model with full access to the bitstream, a Mode 0 variant using only metadata such as codec type, framerate, resoution and bitrate as input, a Mode 1 model using Mode 0 information and frame-type and -size information, and a Hybrid Mode 0 model that is based on Mode 0 metadata and the decoded video pixel information. The models are trained on the authors’ own AVT-PNATS-UHD-1 dataset described in the paper. All models show a highly competitive performance by using AVT-VQDB-UHD-1 as validation dataset, e.g., with the Mode 0 variant yielding a value of 0.890 Pearson Correlation, the Mode 1 model of 0.901, the hybrid no-reference mode 0 model of 0.928 and the model with full bitstream access of 0.942. In addition, all four AVQBits variants are evaluated when applying them out-of-the-box to different media formats such as 360° video, high framerate (HFR) content, or gaming videos. The analysis shows that the ITU-T P.1204.3 and Hybrid Mode 0 instances of AVQBits for the considered use-cases either perform on par with or better than even state-of-the-art full reference, pixel-based models. Furthermore, it is shown that the proposed Mode 0 and Mode 1 variants outperform commonly used no-reference models for the different application scopes. Also, a long-term integration model based on the standardized ITU-T P.1203.3 is presented to estimate ratings of overall audiovisual streaming Quality of Experience (QoE) for sessions of 30 s up to 5 min duration. In the paper, the AVQBits instances with their per-1-sec score output are evaluated as the video quality component of the proposed long-term integration model. All AVQBits variants as well as the long-term integration module are made publicly available for the community for further research