Adaptive Video Streaming Testbed Design for Performance Study and Assessment of QoE

[EN] Hypertext Transfer Protocol adaptive streaming switches between different video qualities, adapting to the network conditions, and avoids stalling streamed frames over high¿oscillation client's throughput improving the users' quality of experience (QoE). Quality of experience has become the most important parameter to lead the service providers to know about the end¿user feedback. Implementing Hypertext Transfer Protocol adaptive streaming applications to find out QoE in real¿life scenarios of vast networks becomes more challenging and complex task regarding to cost, agile, time, and decisions. In this paper, a virtualized network testbed to virtualize various machines to support implementing experiments of adaptive video streaming has been developed. Within the test study, the metrics which demonstrate performance of QoE are investigated, respectively, including initial delay (ie, startup delay at the beginning of playback a video), frequency switches (ie, number of times the quality is changed), accumulative video time (ie, number and length of stalls), CPU usage, and battery energy consumption. Furthermore, the relation between effective parameters of QoS on the aforementioned metrics for different segment length is investigated. Experimental results show that the proposed virtualized system is agile, easy to install and use, and costs less than real testbeds. Moreover, the subjective and objective performance studies of QoE evaluation in the system have proven that the segment lengths of 6 to 8 seconds were faired and more efficient than others according to the investigated parameters.Ministerio de Economia y Competitividad, Grant/Award Number: TIN2014-57991-C3-1-PAbdullah, MTA.; Lloret, J.; Ali, A.; García-García, L. (2018). Adaptive Video Streaming Testbed Design for Performance Study and Assessment of QoE. International Journal of Communication Systems. 1-15. https://doi.org/10.1002/dac.3551S11

Fair-RTT-DAS: A robust and efficient dynamic adaptive streaming over ICN

To sustain the adequate bandwidth demands over rapidly growing multimedia traffic and considering the effectiveness of Information-Centric Networking (ICN), recently, HTTP based Dynamic Adaptive Streaming (DASH) has been introduced over ICN, which significantly increases the network bandwidth utilisation. However, we identified that the inherent features of ICN also causes new vulnerabilities in the network. In this paper, we first propose a novel attack called as Bitrate Oscillation Attack (BOA), which exploits fundamental ICN characteristics: in-network caching and interest aggregation, to disrupt DASH functionality. In particular, the proposed attack forces the bitrate and resolution of video received by the attacked client to oscillate with high frequency and high amplitude during the streaming process. To detect and mitigate BOA, we design and implement a reactive countermeasure called Fair-RTT-DAS. Our solution ensures efficient bandwidth utilisation and improves the user perceived Quality of Experience (QoE) in the presence of varying content source locations. For this purpose, Fair-RTT-DAS consider DASH\u2019s two significant features: round-trip-time (RTT) and throughput fairness. In the presence of BOA in a network, our simulation results show an increase in the annoyance factor in user\u2019s spatial dimension, i.e., increase in oscillation frequency and amplitude. The results also show that our countermeasure significantly alleviates these adverse effects and makes dynamic adaptive streaming friendly to ICN\u2019s implicit features

Content-aware QoE Optimization in MEC-assisted Mobile Video Streaming

The traditional client-based HTTP adaptation strategies do not explicitly coordinate between the clients, servers, and cellular networks. A lack of coordination leads to suboptimal user experience. In addition to optimizing Quality of Experience (QoE), other challenges in adapting HTTP adaptive streaming (HAS) to the cellular environment are overcoming unfair allocation of the video rate and inefficient utilization of the bandwidth under the high-dynamics cellular links. Furthermore, the majority of the adaptive strategies ignore important video content characteristics and HAS client information, such as segment duration, buffer size, and video duration, in the video quality selection process. In this paper, we present a content-aware hybrid multi-access edge computing (MEC)-assisted quality adaptation algorithm by taking advantage of the capabilities of edge cloud computing. The proposed algorithm exploits video content characteristics, HAS client settings, and application-layer information to jointly adapt the bitrates of multiple clients. We design separate strategies to optimize the performance of short and long duration videos. We then demonstrate the efficiency of our algorithm against client-based solutions as well as MEC-assisted algorithms. The proposed algorithm guarantees high QoE, equitably selects video rates for clients, and efficiently utilizes the bandwidth for both short and long duration videos. The results from our extensive experiments reveal that the proposed long video adaptation algorithm outperforms state-of-the-art algorithms, with improvements in average video rate, QoE, fairness, and bandwidth utilization of 0.4%-12.3%, 8%-65%, 3.3%-5.7%, and 60%-130%, respectively. Furthermore, when high bandwidth is available to competing clients, the proposed short video adaptation algorithm improves QoE by 11.1% compared to the long video adaptation algorithm

QoEMultiSDN: Management of Multimedia Services using MPTCP/SR in Softwarized and Virtualized Networks

5G networks are set to empower new user experiences for multimedia streaming services by offering high data rates and low latency. The rapid growth of multimedia services, such as video streaming services over future softwarized 5G networks, poses great challenges for both service providers and network operators regarding service provisioning with high Quality of Experience (QoE). Network evolution towards Software Defined Networking (SDN) and Network Function Virtualization (NFV) provides new opportunities to develop innovative ways to address the challenges. In this paper, we present a novel QoE-aware management scheme based on SDN/NFV by utilizing Multi-Path-TCP/Segment-Routing (MPTCP/SR) techniques and exploring information from both the network and client. The new scheme, entitled QoEMultiSDN, provides an optimized end-to-end QoE for multimedia service delivery. QoEMultiSDN employs two novel strategies to achieve this: (a) QoE-based multisource routing and QoE optimization, and (b) multipath protection and dynamic link-failure-free mechanisms. We implemented the proposed scheme over SDN through Dynamic Adaptive Streaming over HTTP (DASH) experiments using Mininet, POX and OpenDaylight controllers, and compared its performance with MPTCP and regular TCP. Experimental results indicate that the QoEMultiSDN outperforms others in terms of system throughput, failure recovery time and the QoE of the end-user.Science Foundation IrelandInsight Research Centre2021-02-24 JG: broken PDF replacedUpdate citation details during checkdate report - A

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

QoE-Centric Control and Management of Multimedia Services in Software Defined and Virtualized Networks

Multimedia services consumption has increased tremendously since the deployment of 4G/LTE networks. Mobile video services (e.g., YouTube and Mobile TV) on smart devices are expected to continue to grow with the emergence and evolution of future networks such as 5G. The end user’s demand for services with better quality from service providers has triggered a trend towards Quality of Experience (QoE) - centric network management through efficient utilization of network resources. However, existing network technologies are either unable to adapt to diverse changing network conditions or limited in available resources. This has posed challenges to service providers for provisioning of QoE-centric multimedia services. New networking solutions such as Software Defined Networking (SDN) and Network Function Virtualization (NFV) can provide better solutions in terms of QoE control and management of multimedia services in emerging and future networks. The features of SDN, such as adaptability, programmability and cost-effectiveness make it suitable for bandwidth-intensive multimedia applications such as live video streaming, 3D/HD video and video gaming. However, the delivery of multimedia services over SDN/NFV networks to achieve optimized QoE, and the overall QoE-centric network resource management remain an open question especially in the advent development of future softwarized networks. The work in this thesis intends to investigate, design and develop novel approaches for QoE-centric control and management of multimedia services (with a focus on video streaming services) over software defined and virtualized networks. First, a video quality management scheme based on the traffic intensity under Dynamic Adaptive Video Streaming over HTTP (DASH) using SDN is developed. The proposed scheme can mitigate virtual port queue congestion which may cause buffering or stalling events during video streaming, thus, reducing the video quality. A QoE-driven resource allocation mechanism is designed and developed for improving the end user’s QoE for video streaming services. The aim of this approach is to find the best combination of network node functions that can provide an optimized QoE level to end-users through network node cooperation. Furthermore, a novel QoE-centric management scheme is proposed and developed, which utilizes Multipath TCP (MPTCP) and Segment Routing (SR) to enhance QoE for video streaming services over SDN/NFV-based networks. The goal of this strategy is to enable service providers to route network traffic through multiple disjointed bandwidth-satisfying paths and meet specific service QoE guarantees to the end-users. Extensive experiments demonstrated that the proposed schemes in this work improve the video quality significantly compared with the state-of-the- art approaches. The thesis further proposes the path protections and link failure-free MPTCP/SR-based architecture that increases survivability, resilience, availability and robustness of future networks. The proposed path protection and dynamic link recovery scheme achieves a minimum time to recover from a failed link and avoids link congestion in softwarized networks

Dynamic adaptive video streaming with minimal buffer sizes

Recently, adaptive streaming has been widely adopted in video streaming services to improve the Quality-of-Experience (QoE) of video delivery over the Internet. However, state-of-the-art bitrate adaptation achieves satisfactory performance only with extensive buffering of several tens of seconds. This leads to high playback latency in video delivery, which is undesirable especially in the context of live content with a low upper bound on the latency. Therefore, this thesis aims at pushing the application of adaptive streaming to its limit with respect to the buffer size, which is the dominant factor of the streaming latency. In this work, we first address the minimum buffering size required in adaptive streaming, which provides us with guidelines to determine a reasonable low latency for streaming systems. Then, we tackle the fundamental challenge of achieving such a low-latency streaming by developing a novel adaptation algorithm that stabilizes buffer dynamics despite a small buffer size. We also present advanced improvements by designing a novel adaptation architecture with low-delay feedback for the bitrate selection and optimizing the underlying transport layer to offer efficient realtime streaming. Experimental evaluations demonstrate that our approach achieves superior QoE in adaptive video streaming, especially in the particularly challenging case of low-latency streaming.In letzter Zeit setzen immer mehr Anbieter von Video-Streaming im Internet auf adaptives Streaming um die Nutzererfahrung (QoE) zu verbessern. Allerdings erreichen aktuelle Bitrate-Adaption-Algorithmen nur dann eine zufriedenstellende Leistung, wenn sehr große Puffer in der Größenordnung von mehreren zehn Sekunden eingesetzt werden. Dies führt zu großen Latenzen bei der Wiedergabe, was vor allem bei Live-Übertragungen mit einer niedrigen Obergrenze für Verzögerungen unerwünscht ist. Aus diesem Grund zielt die vorliegende Dissertation darauf ab adaptive Streaming-Anwendung im Bezug auf die Puffer-Größe zu optimieren da dies den Hauptfaktor für die Streaming-Latenz darstellt. In dieser Arbeit untersuchen wir zuerst die minimale benötigte Puffer-Größe für adaptives Streaming, was uns ermöglicht eine sinnvolle Untergrenze für die erreichbare Latenz festzulegen. Im nächsten Schritt gehen wir die grundlegende Herausforderung an dieses Optimum zu erreichen. Hierfür entwickeln wir einen neuartigen Adaptionsalgorithmus, der es ermöglicht den Füllstand des Puffers trotz der geringen Größe zu stabilisieren. Danach präsentieren wir weitere Verbesserungen indem wir eine neue Adaptions-Architektur für die Datenraten-Anpassung mit geringer Feedback-Verzögerung designen und das darunter liegende Transportprotokoll optimieren um effizientes Echtzeit-Streaming zu ermöglichen. Durch experimentelle Prüfung zeigen wir, dass unser Ansatz eine verbesserte Nutzererfahrung für adaptives Streaming erreicht, vor allem in besonders herausfordernden Fällen, wenn Streaming mit geringer Latenz gefordert ist