A Quality-of-Experience Index for Streaming Video

Duanmu, Zhengfang; Zeng, Kai; Ma, Kede; Rehman, Abdul; Wang, Zhou

doi:10.1109/jstsp.2016.2608329

Cited by 159 publications

(126 citation statements)

References 31 publications

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“…1 shows the enhancement layer on a scalable stream (Enh1) being dropped at a media adaptaion element while the base layer is delivered.. Existing publications on the scalable extension to H.265 in the context of UHD video, have only made subjective comparisons with previous codecs [39] [40], investigated playback quality across different device types [41] or considered scalable H.265 streaming over HTTP [42] using the Dynamic Adaptive Streaming over HTTP (DASH) [43] protocol rather than the RTP based streaming approach investigated in this paper. None of these recent H.265 QoE studies has proposed a QoE prediction model specifically aimed at the scalable extension to H.265 or the 5G context.…”

Section: ) H265 and Scalable H265 Video Coding Standards And Qoe mentioning

confidence: 99%

5G-QoE: QoE Modelling for Ultra-HD Video Streaming in 5G Networks

Nightingale

Salvá-García

Calero

et al. 2018

IEEE Trans. on Broadcast.

153

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This version is available at https://strathprints.strath.ac.uk/65207/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. IEEE Transactions on BroadcastingPaper Identification Number BTS-yr-xxx (DOUBLE-CLICK HERE TO EDIT) 1  Abstract-Traffic on future Fifth-Generation (5G) mobile networks is predicted to be dominated by challenging video applications such as mobile broadcasting, remote surgery and augmented reality, demanding real-time and ultra-high quality delivery. Two of the main expectations of 5G networks are that they will be able to handle Ultra High Definition (UHD) video streaming and that they will deliver services that meet the requirements of the end user's perceived quality by adopting Quality of Experience (QoE) aware network management approaches. This paper proposes a 5G-QoE framework to address the QoE modelling for UHD video flows in 5G networks. Particularly, it focuses on providing a QoE prediction model that is both sufficiently accurate and of low enough complexity to be employed as a continuous real-time indicator of the 'health' of video applications flows at the scale required in future 5G networks. The model has been developed and implemented as part of the EU 5G PPP SELFNET autonomic management framework, where it provides a primary indicator of the likely perceptual quality of UHD video application flows traversing a realistic multi-tenanted 5G mobile edge network testbed. The proposed 5G-QoE framework has been implemented in the 5G testbed, and the high accuracy of QoE prediction has been validated through comparing the predicted QoE values with not only subjective testing results but also empirical measurements in the testbed. As such, 5G-QoE would enable a holistic video flow self-optimisation system employing the cutting-edge Scalable H.265 video encoding to transmit UHD video applications in a QoE-aware manner.Index Terms-QoE, 5G networks, video streaming, UHD.

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Section: ) H265 and Scalable H265 Video Coding Standards And Qoe mentioning

confidence: 99%

5G-QoE: QoE Modelling for Ultra-HD Video Streaming in 5G Networks

Nightingale

Salvá-García

Calero

et al. 2018

IEEE Trans. on Broadcast.

153

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“…The other reason is that the experiment was centered on the quality switching event itself, and one stimulus only contained a single switching event. Similar video stimulus durations were used in the recent work of Duanmu et al [61], addressing streaming QoE. The evaluation was performed on a 5-point comparison scale ("Much worse (−2)", "Worse (−1)", "Same (0)", "Better (+1)", "Much better (+2)").…”

Section: B Subjective Quality Assessment Tasksmentioning

confidence: 99%

Evaluation of the Concept of Dynamic Adaptive Streaming of Light Field Video

Kara

Cserkaszky

Martini

et al. 2018

IEEE Trans. on Broadcast.

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Abstract-Light field visualization has progressed and developed significantly in the past years. At the time of this paper, light field displays are utilized in the industry and they are commercially available as well. Although their appearance on the consumer market is approaching, many potential applications of light field technology have not yet been addressed, such as video streaming. In this paper, we present our research on the dynamic adaptive streaming of light field video. In order to evaluate the presented concept of quality switching, we carried out a series of subjective tests, where test participants were shown light field videos containing stallings and switches in spatial and angular resolution.

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“…Select the chunks size 2 seconds duration related to the above experiment has done for different segment length according to the proposed assessment methodology. Three types of devices are selected to apply the experiments, such as TV, Laptop, and mobile device [147]. Results as shown in Figure 5.7, the evaluation of subjective metric (MOS) using mobile device is higher than laptop and TV.…”

Section: Results Analysismentioning

confidence: 99%

“…Subsequently, four participants are removed based on the subject removal scheme, which is suggested in [147], resulting in 30 valid participants. After participants are removed from the scheme, Z-scores are linearly rescaled to lie in the range of [1,5] (See Table 5.4).…”

Section: Data Processingmentioning

confidence: 99%

“…Therefore, the same sequences of the previous experiment have been used to evaluate the objective QoE as shown in Table 5.2. We selected three important metrics such as PSNR, SSIM and VQM for HTTP adaptive streaming [147]. As shown in Figure 5.8, according to Table 5.5 we find only SSIM objective metric for all representations of same video.…”

Section: Experiments For Objective Assessmentmentioning

confidence: 99%

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Smart Client-Server Protocol and Architecture for Adaptive Multimedia Streaming

Abdullah¹

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In recent years, multimedia services consumption has increased and it is expected that this trend will continue in the near future, becoming the evaluation of Quality of Experience (QoE) as a very important issue for assessing the quality of providers' services. In this sense, the optimization of the QoE is progressively receiving much attention considering that current solutions are not based on the adaptation, feasibility, cost-effectiveness and reliability.The present dissertation is focused on the characterization, design, development and evaluation of different multimedia applications aimed to optimize the QoE.Therefore, this work investigates the influence that the networks infrastructure, the videos' characteristics and the users' terminals present on QoE of the current Internet multimedia services. The work is based on a comprehensive research of subjective and objective assessments in heterogeneous networks. Challenges and research questions related to the state of the art are discussed in this dissertation.In the first phase of this dissertation, we design a test methodology for assessing QoE of live video streaming and video on demand platforms to be transmitted over Wi-Fi and cellular networks. From this initial step, we will propound the related research issues and questions to solve along this dissertation. Our methodology considers the use of subjective and objective metrics to evaluate the QoE perceived by end-users. A set of laboratory experiments is conducted where our proposed methodology is applied. The obtained results are gathered and analyzed to extract the relations between Quality of Service (QoS) and QoE. From the results, we propose a QoSQoE mapping which allows predicting QoE.In the next phase of the research, we develop QoE-optimization algorithms based on network system management for Wi-Fi and cellular networks. The algorithms use the key parameters that were taken into account for QoE assessment. The goal of these algorithms is to provide a flexible management system for the networks in order to achieve the desirable tradeoff between QoE maximization and resource usage efficiency. IILastly, the system testbed is designed in order to evaluate the performance of generic multimedia services applications for the different environments under test. The system testbed is based on virtualization approach; it uses the shared resources of a physical hardware to virtualize all components. The virtualized testbed provides virtualized network functions for the different scenarios such as the Internet (Content Delivery NetworksCDNs) and wireless networks. Therefore, lightweight protocols and agile mechanisms are adopted in the system to provide enhanced service to endusers. The QoE results are reported to the service providers according to the parameters defined in the evaluation process. As a result, we have obtained a cost-effective system, which is considered as a feasible way for test evaluation. III ResumenEn los últimos años, el consumo de servicios multimedia ha aumentado y se pr...

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A Quality-of-Experience Index for Streaming Video

Cited by 159 publications

References 31 publications

5G-QoE: QoE Modelling for Ultra-HD Video Streaming in 5G Networks

5G-QoE: QoE Modelling for Ultra-HD Video Streaming in 5G Networks

Evaluation of the Concept of Dynamic Adaptive Streaming of Light Field Video

Smart Client-Server Protocol and Architecture for Adaptive Multimedia Streaming

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