Fair Quality of Experience (QoE) Measurements Related with Networking Technologies

[Invited Talk] Eighth International Conference on Wired/Wireless Internet Communications (June 1-3, Luleå, Sweden)Proceeding of: 8th International Conference, WWIC 2010, Lulea, Sweden, June 1-3, 2010This paper addresses the topic of Fair QoE measurements in networking. The research of new solutions in networking is oriented to improve the user experience. Any application or service can be im- proved and the deployment of new solutions is mandatory to get the user satisfaction. However, different solutions exist; thus, it is necessary to select the most suitable ones. Nevertheless, this selection is difficult to make since the QoE is subjective and the comparison among different technologies is not trivial. The aim of this paper is to give an overview on how to perform fair QoE measurements to facilitate the study and re- search of new networking solutions and paradigms. However, previously to address this problem, an overview about how networking affects to the QoE is provided.This work has been funded by the CONTENT NoE from the European Commission (FP6- 2005-IST-41) and by the Ministry of Science and Innovation under the CON- PARTE project (MEC, TEC2007-67966-C03-03/TCM) and T2C2 project grant (TIN2008-06739-C04-01).Publicad

QoE-Based Low-Delay Live Streaming Using Throughput Predictions

Recently, HTTP-based adaptive streaming has become the de facto standard for video streaming over the Internet. It allows clients to dynamically adapt media characteristics to network conditions in order to ensure a high quality of experience, that is, minimize playback interruptions, while maximizing video quality at a reasonable level of quality changes. In the case of live streaming, this task becomes particularly challenging due to the latency constraints. The challenge further increases if a client uses a wireless network, where the throughput is subject to considerable fluctuations. Consequently, live streams often exhibit latencies of up to 30 seconds. In the present work, we introduce an adaptation algorithm for HTTP-based live streaming called LOLYPOP (Low-Latency Prediction-Based Adaptation) that is designed to operate with a transport latency of few seconds. To reach this goal, LOLYPOP leverages TCP throughput predictions on multiple time scales, from 1 to 10 seconds, along with an estimate of the prediction error distribution. In addition to satisfying the latency constraint, the algorithm heuristically maximizes the quality of experience by maximizing the average video quality as a function of the number of skipped segments and quality transitions. In order to select an efficient prediction method, we studied the performance of several time series prediction methods in IEEE 802.11 wireless access networks. We evaluated LOLYPOP under a large set of experimental conditions limiting the transport latency to 3 seconds, against a state-of-the-art adaptation algorithm from the literature, called FESTIVE. We observed that the average video quality is by up to a factor of 3 higher than with FESTIVE. We also observed that LOLYPOP is able to reach a broader region in the quality of experience space, and thus it is better adjustable to the user profile or service provider requirements.Comment: Technical Report TKN-16-001, Telecommunication Networks Group, Technische Universitaet Berlin. This TR updated TR TKN-15-00

QoE on media deliveriy in 5G environments

231 p.5G expandirá las redes móviles con un mayor ancho de banda, menor latencia y la capacidad de proveer conectividad de forma masiva y sin fallos. Los usuarios de servicios multimedia esperan una experiencia de reproducción multimedia fluida que se adapte de forma dinámica a los intereses del usuario y a su contexto de movilidad. Sin embargo, la red, adoptando una posición neutral, no ayuda a fortalecer los parámetros que inciden en la calidad de experiencia. En consecuencia, las soluciones diseñadas para realizar un envío de tráfico multimedia de forma dinámica y eficiente cobran un especial interés. Para mejorar la calidad de la experiencia de servicios multimedia en entornos 5G la investigación llevada a cabo en esta tesis ha diseñado un sistema múltiple, basado en cuatro contribuciones.El primer mecanismo, SaW, crea una granja elástica de recursos de computación que ejecutan tareas de análisis multimedia. Los resultados confirman la competitividad de este enfoque respecto a granjas de servidores. El segundo mecanismo, LAMB-DASH, elige la calidad en el reproductor multimedia con un diseño que requiere una baja complejidad de procesamiento. Las pruebas concluyen su habilidad para mejorar la estabilidad, consistencia y uniformidad de la calidad de experiencia entre los clientes que comparten una celda de red. El tercer mecanismo, MEC4FAIR, explota las capacidades 5G de analizar métricas del envío de los diferentes flujos. Los resultados muestran cómo habilita al servicio a coordinar a los diferentes clientes en la celda para mejorar la calidad del servicio. El cuarto mecanismo, CogNet, sirve para provisionar recursos de red y configurar una topología capaz de conmutar una demanda estimada y garantizar unas cotas de calidad del servicio. En este caso, los resultados arrojan una mayor precisión cuando la demanda de un servicio es mayor

QoE estimation for different adaptive streaming techniques in mobile networks

Video services are becoming more and more popular for mobile network users and require greater and greater resources and provisions from telecommunications service providers. But operators suffer from problems of interoperability between the different adaptive transmissions techniques they employ in an attempt to satisfy the quality of experience (QoE) of the service provided to users and improve network performance. This article presents a comparison of four such streaming techniques - DASH (dynamic adaptive streaming over HTTP), HDS (HTTP dynamic streaming), HLS (HTTP2 live streaming) and HSS (HTTP smooth streaming) - used in a live video playback by a user in different test scenarios on an emulated long-term evolution (LTE) network. Comparison of performance was carried out using the mean opinion score (MOS) metric calculated based on ITU-T Recommendation P.1203. The streaming techniques that performed best in each of the different test scenarios are revealed.El servicio de video es cada vez más popular por parte de los usuarios de redes móviles, además exige mayores recursos y prestaciones por parte de los proveedores de servicios de telecomunicaciones. Para satisfacer la calidad de la experiencia del servicio suministrado a los usuarios - QoE y mejorar el rendimiento de las redes, los operadores utilizan diferentes técnicas de transmisión adaptativa, las cuales presentan inconvenientes de interoperabilidad entre ellas.  En este artículo se presenta una comparación de las técnicas de streaming DASH (dynamic adaptive streaming over HTTP), HDS (HTTP dynamic streaming), HLS (HTTP2 live streaming) and HSS (HTTP smooth streaming) empleadas en la reproducción de vídeo en vivo por parte de un usuario en diferentes escenarios de prueba, en una red LTE emulada. La comparación de desempeño se realiza mediante la métrica de la MOS calculada a partir de la Recomendación ITU-T P.1203. Se presenta para los diferentes escenarios bajo prueba, la técnica de streaming que mejor desempeño obtiene

QoE estimation for different adaptive streaming techniques in mobile networks

Video services are becoming more and more popular for mobile network users and require greater and greater resources and provisions from telecommunications service providers. But operators suffer from problems of interoperability between the different adaptive transmissions techniques they employ in an attempt to satisfy the quality of experience (QoE) of the service provided to users and improve network performance. This article presents a comparison of four such streaming techniques - DASH (dynamic adaptive streaming over HTTP), HDS (HTTP dynamic streaming), HLS (HTTP2 live streaming) and HSS (HTTP smooth streaming) - used in a live video playback by a user in different test scenarios on an emulated long-term evolution (LTE) network. Comparison of performance was carried out using the mean opinion score (MOS) metric calculated based on ITU-T Recommendation P.1203. The streaming techniques that performed best in each of the different test scenarios are revealed.El servicio de video es cada vez más popular por parte de los usuarios de redes móviles, además exige mayores recursos y prestaciones por parte de los proveedores de servicios de telecomunicaciones. Para satisfacer la calidad de la experiencia del servicio suministrado a los usuarios - QoE y mejorar el rendimiento de las redes, los operadores utilizan diferentes técnicas de transmisión adaptativa, las cuales presentan inconvenientes de interoperabilidad entre ellas.  En este artículo se presenta una comparación de las técnicas de streaming DASH (dynamic adaptive streaming over HTTP), HDS (HTTP dynamic streaming), HLS (HTTP2 live streaming) and HSS (HTTP smooth streaming) empleadas en la reproducción de vídeo en vivo por parte de un usuario en diferentes escenarios de prueba, en una red LTE emulada. La comparación de desempeño se realiza mediante la métrica de la MOS calculada a partir de la Recomendación ITU-T P.1203. Se presenta para los diferentes escenarios bajo prueba, la técnica de streaming que mejor desempeño obtiene

Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

IEEE Access Volume 3, 2015, Article number 7217798, Pages 1512-1530 Open Access Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article) Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc a Department of Information Engineering, University of Padua, Padua, Italy b Department of General Psychology, University of Padua, Padua, Italy c IRCCS San Camillo Foundation, Venice-Lido, Italy View additional affiliations View references (107) Abstract In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network