Minimizing the impact of delay on live SVC-based HTTP adaptive streaming services

HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for Over-The-Top video streaming services. Video content is temporally split into segments which are offered at multiple qualities to the clients. These clients autonomously select the quality layer matching the current state of the network through a quality selection heuristic. Recently, academia and industry have begun evaluating the feasibility of adopting layered video coding for HAS. Instead of downloading one file for a certain quality level, scalable video streaming requires downloading several interdependent layers to obtain the same quality. This implies that the base layer is always downloaded and is available for playout, even when throughput fluctuates and enhancement layers can not be downloaded in time. This layered video approach can help in providing better service quality assurance for video streaming. However, adopting scalable video coding for HAS also leads to other issues, since requesting multiple files over HTTP leads to an increased impact of the end-to-end delay and thus on the service provided to the client. This is even worse in a Live TV scenario where the drift on the live signal should be minimized, requiring smaller segment and buffer sizes. In this paper, we characterize the impact of delay on several measurement-based heuristics. Furthermore, we propose several ways to overcome the end-to-end delay issues, such as parallel and pipelined downloading of segment layers, to provide a higher quality for the video service

Provider-Controlled Bandwidth Management for HTTP-based Video Delivery

Over the past few years, a revolution in video delivery technology has taken place as mobile viewers and over-the-top (OTT) distribution paradigms have significantly changed the landscape of video delivery services. For decades, high quality video was only available in the home via linear television or physical media. Though Web-based services brought video to desktop and laptop computers, the dominance of proprietary delivery protocols and codecs inhibited research efforts. The recent emergence of HTTP adaptive streaming protocols has prompted a re-evaluation of legacy video delivery paradigms and introduced new questions as to the scalability and manageability of OTT video delivery. This dissertation addresses the question of how to enable for content and network service providers the ability to monitor and manage large numbers of HTTP adaptive streaming clients in an OTT environment. Our early work focused on demonstrating the viability of server-side pacing schemes to produce an HTTP-based streaming server. We also investigated the ability of client-side pacing schemes to work with both commodity HTTP servers and our HTTP streaming server. Continuing our client-side pacing research, we developed our own client-side data proxy architecture which was implemented on a variety of mobile devices and operating systems. We used the portable client architecture as a platform for investigating different rate adaptation schemes and algorithms. We then concentrated on evaluating the network impact of multiple adaptive bitrate clients competing for limited network resources, and developing schemes for enforcing fair access to network resources. The main contribution of this dissertation is the definition of segment-level client and network techniques for enforcing class of service (CoS) differentiation between OTT HTTP adaptive streaming clients. We developed a segment-level network proxy architecture which works transparently with adaptive bitrate clients through the use of segment replacement. We also defined a segment-level rate adaptation algorithm which uses download aborts to enforce CoS differentiation across distributed independent clients. The segment-level abstraction more accurately models application-network interactions and highlights the difference between segment-level and packet-level time scales. Our segment-level CoS enforcement techniques provide a foundation for creating scalable managed OTT video delivery services

Quality of experience-centric management of adaptive video streaming services : status and challenges

Video streaming applications currently dominate Internet traffic. Particularly, HTTP Adaptive Streaming ( HAS) has emerged as the dominant standard for streaming videos over the best-effort Internet, thanks to its capability of matching the video quality to the available network resources. In HAS, the video client is equipped with a heuristic that dynamically decides the most suitable quality to stream the content, based on information such as the perceived network bandwidth or the video player buffer status. The goal of this heuristic is to optimize the quality as perceived by the user, the so-called Quality of Experience (QoE). Despite the many advantages brought by the adaptive streaming principle, optimizing users' QoE is far from trivial. Current heuristics are still suboptimal when sudden bandwidth drops occur, especially in wireless environments, thus leading to freezes in the video playout, the main factor influencing users' QoE. This issue is aggravated in case of live events, where the player buffer has to be kept as small as possible in order to reduce the playout delay between the user and the live signal. In light of the above, in recent years, several works have been proposed with the aim of extending the classical purely client-based structure of adaptive video streaming, in order to fully optimize users' QoE. In this article, a survey is presented of research works on this topic together with a classification based on where the optimization takes place. This classification goes beyond client-based heuristics to investigate the usage of server-and network-assisted architectures and of new application and transport layer protocols. In addition, we outline the major challenges currently arising in the field of multimedia delivery, which are going to be of extreme relevance in future years

An HTTP/2 push-based approach for low-latency live streaming with super-short segments

Over the last years, streaming of multimedia content has become more prominent than ever. To meet increasing user requirements, the concept of HTTP Adaptive Streaming (HAS) has recently been introduced. In HAS, video content is temporally divided into multiple segments, each encoded at several quality levels. A rate adaptation heuristic selects the quality level for every segment, allowing the client to take into account the observed available bandwidth and the buffer filling level when deciding the most appropriate quality level for every new video segment. Despite the ability of HAS to deal with changing network conditions, a low average quality and a large camera-to-display delay are often observed in live streaming scenarios. In the meantime, the HTTP/2 protocol was standardized in February 2015, providing new features which target a reduction of the page loading time in web browsing. In this paper, we propose a novel push-based approach for HAS, in which HTTP/2's push feature is used to actively push segments from server to client. Using this approach with video segments with a sub-second duration, referred to as super-short segments, it is possible to reduce the startup time and end-to-end delay in HAS live streaming. Evaluation of the proposed approach, through emulation of a multi-client scenario with highly variable bandwidth and latency, shows that the startup time can be reduced with 31.2% compared to traditional solutions over HTTP/1.1 in mobile, high-latency networks. Furthermore, the end-to-end delay in live streaming scenarios can be reduced with 4 s, while providing the content at similar video quality

Multimedia delivery in the future internet

The term “Networked Media” implies that all kinds of media including text, image, 3D graphics, audio and video are produced, distributed, shared, managed and consumed on-line through various networks, like the Internet, Fiber, WiFi, WiMAX, GPRS, 3G and so on, in a convergent manner [1]. This white paper is the contribution of the Media Delivery Platform (MDP) cluster and aims to cover the Networked challenges of the Networked Media in the transition to the Future of the Internet. Internet has evolved and changed the way we work and live. End users of the Internet have been confronted with a bewildering range of media, services and applications and of technological innovations concerning media formats, wireless networks, terminal types and capabilities. And there is little evidence that the pace of this innovation is slowing. Today, over one billion of users access the Internet on regular basis, more than 100 million users have downloaded at least one (multi)media file and over 47 millions of them do so regularly, searching in more than 160 Exabytes1 of content. In the near future these numbers are expected to exponentially rise. It is expected that the Internet content will be increased by at least a factor of 6, rising to more than 990 Exabytes before 2012, fuelled mainly by the users themselves. Moreover, it is envisaged that in a near- to mid-term future, the Internet will provide the means to share and distribute (new) multimedia content and services with superior quality and striking flexibility, in a trusted and personalized way, improving citizens’ quality of life, working conditions, edutainment and safety. In this evolving environment, new transport protocols, new multimedia encoding schemes, cross-layer inthe network adaptation, machine-to-machine communication (including RFIDs), rich 3D content as well as community networks and the use of peer-to-peer (P2P) overlays are expected to generate new models of interaction and cooperation, and be able to support enhanced perceived quality-of-experience (PQoE) and innovative applications “on the move”, like virtual collaboration environments, personalised services/ media, virtual sport groups, on-line gaming, edutainment. In this context, the interaction with content combined with interactive/multimedia search capabilities across distributed repositories, opportunistic P2P networks and the dynamic adaptation to the characteristics of diverse mobile terminals are expected to contribute towards such a vision. Based on work that has taken place in a number of EC co-funded projects, in Framework Program 6 (FP6) and Framework Program 7 (FP7), a group of experts and technology visionaries have voluntarily contributed in this white paper aiming to describe the status, the state-of-the art, the challenges and the way ahead in the area of Content Aware media delivery platforms

Quality-driven resource utilization methods for video streaming in wireless communication networks

This research is focused on the optimisation of resource utilisation in wireless mobile networks with the consideration of the users’ experienced quality of video streaming services. The study specifically considers the new generation of mobile communication networks, i.e. 4G-LTE, as the main research context. The background study provides an overview of the main properties of the relevant technologies investigated. These include video streaming protocols and networks, video service quality assessment methods, the infrastructure and related functionalities of LTE, and resource allocation algorithms in mobile communication systems. A mathematical model based on an objective and no-reference quality assessment metric for video streaming, namely Pause Intensity, is developed in this work for the evaluation of the continuity of streaming services. The analytical model is verified by extensive simulation and subjective testing on the joint impairment effects of the pause duration and pause frequency. Various types of the video contents and different levels of the impairments have been used in the process of validation tests. It has been shown that Pause Intensity is closely correlated with the subjective quality measurement in terms of the Mean Opinion Score and this correlation property is content independent. Based on the Pause Intensity metric, an optimised resource allocation approach is proposed for the given user requirements, communication system specifications and network performances. This approach concerns both system efficiency and fairness when establishing appropriate resource allocation algorithms, together with the consideration of the correlation between the required and allocated data rates per user. Pause Intensity plays a key role here, representing the required level of Quality of Experience (QoE) to ensure the best balance between system efficiency and fairness. The 3GPP Long Term Evolution (LTE) system is used as the main application environment where the proposed research framework is examined and the results are compared with existing scheduling methods on the achievable fairness, efficiency and correlation. Adaptive video streaming technologies are also investigated and combined with our initiatives on determining the distribution of QoE performance across the network. The resulting scheduling process is controlled through the prioritization of users by considering their perceived quality for the services received. Meanwhile, a trade-off between fairness and efficiency is maintained through an online adjustment of the scheduler’s parameters. Furthermore, Pause Intensity is applied to act as a regulator to realise the rate adaptation function during the end user’s playback of the adaptive streaming service. The adaptive rates under various channel conditions and the shape of the QoE distribution amongst the users for different scheduling policies have been demonstrated in the context of LTE. Finally, the work for interworking between mobile communication system at the macro-cell level and the different deployments of WiFi technologies throughout the macro-cell is presented. A QoEdriven approach is proposed to analyse the offloading mechanism of the user’s data (e.g. video traffic) while the new rate distribution algorithm reshapes the network capacity across the macrocell. The scheduling policy derived is used to regulate the performance of the resource allocation across the fair-efficient spectrum. The associated offloading mechanism can properly control the number of the users within the coverages of the macro-cell base station and each of the WiFi access points involved. The performance of the non-seamless and user-controlled mobile traffic offloading (through the mobile WiFi devices) has been evaluated and compared with that of the standard operator-controlled WiFi hotspots

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