Changing network conditions pose severe problems to video streaming in the Internet. HTTP adaptive streaming (HAS) is a technology employed by numerous video services that relieves these issues by adapting the video to the current network conditions. It enables service providers to improve resource utilization and Quality of Experience (QoE) by incorporating information from different layers in order to deliver and adapt a video in its best possible quality. Thereby, it allows taking into account end user device capabilities, available video quality levels, current network conditions, and current server load. For end users, the major benefits of HAS compared to classical HTTP video streaming are reduced interruptions of the video playback and higher bandwidth utilization, which both generally result in a higher QoE. Adaptation is possible by changing the frame rate, resolution, or quantization of the video, which can be done with various adaptation strategies and related client- and server-side actions. The technical development of HAS, existing open standardized solutions, but also proprietary solutions are reviewed in this paper as fundamental to derive the QoE influence factors that emerge as a result of adaptation. The main contribution is a comprehensive survey of QoE related works from human computer interaction and networking domains, which are structured according to the QoE impact of video adaptation. To be more precise, subjective studies that cover QoE aspects of adaptation dimensions and strategies are revisited. As a result, QoE influence factors of HAS and corresponding QoE models are identified, but also open issues and conflicting results are discussed. Furthermore, technical influence factors, which are often ignored in the context of HAS, affect perceptual QoE influence factors and are consequently analyzed. This survey gives the reader an overview of the current state of the art and recent developments. At the same time, it targets networking researchers who develop new solutions for HTTP video streaming or assess video streaming from a user centric point of view. Therefore, this paper is a major step toward truly improving HAS.

A Survey on Quality of Experience of HTTP Adaptive Streaming

Quality of Service (QoS) in Software Defined Networking (SDN)

In this survey, we present state-of-the-art bitrate adaptation algorithms for HTTP adaptive streaming (HAS). As a key distinction from other streaming approaches, the bitrate adaptation algorithms in HAS are chiefly executed at each client, i.e. , in a distributed manner. The objective of these algorithms is to ensure a high quality of experience (QoE) for viewers in the presence of bandwidth fluctuations due to factors like signal strength, network congestion, network reconvergence events, etc. While such fluctuations are common in public Internet, they can also occur in home networksor even managed networks where there is often admission control and QoS tools. Bitrate adaptation algorithms may take factors like bandwidth estimations, playback buffer fullness, device features, viewer preferences, and content features into account, albeit with different weights. Since the viewer’s QoE needs to be determined in real-time during playback, objective metrics are generally used including number of buffer stalls, duration of startup delay, frequency and amount of quality oscillations, and video instability. By design, the standards for HAS do not mandate any particular adaptation algorithm, leaving it to system builders to innovate and implement their own method. This survey provides an overview of the different methods proposed over the last several years.

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A Survey on Bitrate Adaptation Schemes for Streaming Media Over HTTP

This book concerns the process of designing IT systems for large, complex purposes. It is structured as a textbook-style introduction to the processes required for use in university teaching and for self-study purposes by people working in the field of IT system development. The content of the book is the fruit of a decade of research by the authors in their pursuit of a method to plan and design IT projects that will provide sustainable, useful solutions to the needs of the companies for which they are commissioned.

Participatory IT Design: Designing for Business and Workplace Realities

Video streaming is an increasingly popular way to consume media content. Adaptive video streaming is an emerging delivery technology which aims to increase user QoE and maximise connection utilisation. Many implementations naively estimate bandwidth from a one-sided client perspective, without taking into account other devices in the network. This behaviour results in unfairness and could potentially lower QoE for all clients. We propose an OpenFlow-assisted QoE Fairness Framework that aims to fairly maximise the QoE of multiple competing clients in a shared network environment. By leveraging a Software Defined Networking technology, such as OpenFlow, we provide a control plane that orchestrates this functionality. The evaluation of our approach in a home networking scenario introduces user-level fairness and network stability, and illustrates the optimisation of QoE across multiple devices in a network.

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Towards network-wide QoE fairness using openflow-assisted adaptive video streaming

Through years of development Content Networks (CN) have become more sophisticated and more technically diverse. Modern CN are designed to be more adaptive to communication environment, devices and user requirements. However, one open issue is the still fluctuating quality of service provision. As a result user experience can be negatively affected. In order to maintain a satisfactory level of user experience it is crucial to develop a feasible solution to measure the extent to which video services meet users' expectation. Assessing video quality with respect to users' subjective opinions is a complex task. In this paper we address challenges of this task and design an integrated framework using a number of comprehensive functional modules. Our framework integrates objective quality assessment models of Artifacts Measurement (AM) and Quality of Delivery (QoD) approaches. Only the fittest models are activated by the framework considering requirements of individual evaluation tasks. We also introduce our recent work of realising key functional modules of the framework. Joint subjective experiments between two institutes have also been carried out for the purpose of model implementation and evaluation. Results from experiments verify the concept of an integrated framework and show the effectiveness of its key modules in estimating the quality level of video services.

Framework for the integrated video quality assessment

The growing demand for online distribution of high quality and high throughput content is dominating today’s Internet infrastructure. This includes both production and user-generated media. Among the myriad of media distribution mechanisms, HTTP adaptive streaming (HAS) is becoming a popular choice for multi-screen and multi-bitrate media services over heterogeneous networks. HAS applications often compete for network resources without any coordination between each other. This leads to quality of experience (QoE) fluctuations on delivered content, and unfairness between end users, while new network protocols, technologies, and architectures, such as software defined networking (SDN), are being developed for the future Internet. The programmability, flexibility, and openness of these emerging developments can greatly assist the distribution of video over the Internet. This is driven by the increasing consumer demands and QoE requirements. This paper introduces a novel user-level fairness model UFair and its hierarchical variant UFair $^{\text {HA}}$ , which orchestrate HAS media streams using emerging network architectures and incorporate three fairness metrics (video quality, switching impact, and cost efficiency) to achieve user-level fairness in video distribution. UFair $^\text {HA}$ has also been implemented in a purpose-built SDN testbed using open technologies, including OpenFlow. Experimental results demonstrate the performance and feasibility of our design for video distribution over future networks.

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A Scalable User Fairness Model for Adaptive Video Streaming Over SDN-Assisted Future Networks

Modern IPTV services are comprised of multiple comprehensive service elements in the entire content delivery chain to maximise the efficiency in networking. Audio-visual content may experience various types of impairments during content ingest, processing, distribution and reception. While some impairments do not cause noticeable distortions to the delivered content, many others such as the network transmission loss can be highly detrimental to the user experience in content consumption. In order to optimise service quality and to provide a benchmarking platform to evaluate the designs for future audio-visual content distribution system, a quality evaluation framework is essential. We introduce such an evaluation framework to assess video service with respect of user perception, while supporting service diagnosis to identify root-causes of any detected quality degradation. Compared with existing QoE frameworks, our solution offers an advanced but practical design for the real-time analysis of IPTV services in multiple service layers.

Quality evaluation in peer-to-peer IPTV services

Existing network QoS models do not sufficiently reflect the challenges faced by high-throughput, always-on, inelastic multimedia applications. In this paper, a utility-based QoS model is proposed as a user layer extension to existing communication QoS models to better assess the requirements of multimedia applications and manage the QoS provisioning of multimedia flows. Network impairment utility functions are derived from user experiments and combined to application utility functions to evaluate the application quality. Simulation is used to demonstrate the validity of the proposed QoS model.

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Mu Mu

Papers

Towards network-wide QoE fairness using openflow-assisted adaptive video streaming

Framework for the integrated video quality assessment

A Scalable User Fairness Model for Adaptive Video Streaming Over SDN-Assisted Future Networks

Quality evaluation in peer-to-peer IPTV services

A Utility-Based QoS Model for Emerging Multimedia Applications