Academic literature on the topic 'QoE, QoS, Monitoring, Videoqualität'
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Journal articles on the topic "QoE, QoS, Monitoring, Videoqualität"
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Truong, Thu-Huong, Nguyen Huu Thanh, and Nguyen Tai Hung. "Pragmatic Correlations of Quality-of-Experience and Quality-of-Service in IMS-Based IPTV Network." International Journal of Distributed Systems and Technologies 4, no. 1 (2013): 29–42. http://dx.doi.org/10.4018/jdst.2013010103.
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To let the service system react quickly on customers’ perception or user experience while using the service, mapping functions between Quality of Experience (QoE) and Quality of Service (QoS) are strongly required in purpose of building up an intelligent QoE control system upon adjusting QoS parameters. This article studies the changing behavior of QoE with respect to changes of QoS parameters in the context of video streaming service in an IP Multimedia Subsystem-based IP Television network (IMS-based IPTV network). The article is, in fact, an extended version of the paper published by the same authors (Thu-Huong Truong, 2012). In (Thu-Huong Truong, 2012), the authors studied QoE in both terms of Mean Opinion Scores and VQM as functions of each single QoS parameter such as: loss, jitter, and delay. In this extended content, the correlation between QoE and multiple QoS parameters will be introduced. The QoE-QoS correlation could be a significant first step to build a smart QoE monitoring and control mechanism as an added value to promote the IMS-based IPTV network.
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Frnda, Jaroslav, Jan Nedoma, Jan Vanus, and Radek Martinek. "A Hybrid QoS-QoE Estimation System for IPTV Service." Electronics 8, no. 5 (2019): 585. http://dx.doi.org/10.3390/electronics8050585.
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The internet protocol television service (IPTV) has become a key product for internet service providers (ISP), offering several benefits to both ISP and end-users. Because packet networks based on internet protocol have not been prepared for time-sensitive services, such as voice or video, packet networks have had to adopt several mechanisms to secure minimal transmission standards in the form of data stream prioritization. There are two commonly used approaches for video quality assessment. The first approach needs an original source for comparison (full-reference objective metrics), and the second one requires observers for subjective evaluation of video quality. Both approaches are impractical in real-time transmission because it is difficult to transform an objective score into a subjective quality perception, and on the other hand, subjective tests are not able to be performed immediately. Since many countries worldwide put IPTV on the same level as other broadcasting systems (e.g., terrestrial, cable, or satellite), IPTV services are subject to regulation by the national regulation authority. This results in the need to prepare service qualitative criteria and monitoring tools capable of measuring end-user satisfaction levels. Our proposed model combines the principles of both assessment approaches, which results in an effective monitoring solution. Therefore, the main contribution of the created system is to offer a monitoring tool able to analyze the features extracted from the video sequence and transmission system and promptly translate their impact into a subjective point of view.
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Skorin-Kapov, Lea, Ognjen Dobrijevic, and Domagoj Piplica. "Towards Evaluating the Quality of Experience of Remote Patient Monitoring Services." International Journal of Mobile Human Computer Interaction 6, no. 4 (2014): 59–89. http://dx.doi.org/10.4018/ijmhci.2014100104.
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The applicability of advanced mobile technologies in the m-Health domain has led to a number of studies and (limited) commercial products supporting delivery of health services to remote users. A key issue regarding successful delivery and acceptance of such services is meeting their Quality of Service (QoS) and Quality of Experience (QoE) requirements, focusing on technical aspects and end user perceived quality, respectively. In this paper, the authors address the topic of evaluating QoE for non-emergency remote patient monitoring services. They identify relevant QoE influence factors and metrics, and present the results of a QoE evaluation study, whereby they focus on usability aspects. The study involves 26 users testing a prototype version of the Ericsson Mobile Health service, which is based on a smartphone application and measurement of vital signs via medical sensors. The results show a strong correlation between QoE and: perceived effectiveness of the mobile interface (regarding both adequacy of smartphone screen size and smartphone application navigational support), perceived ease of conducting a blood pressure measurement task, and user motivation for service usage.
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Tommasi, Franco, Valerio De Luca, and Catiuscia Melle. "QoS monitoring in real-time streaming overlays based on lock-free data structures." Multimedia Tools and Applications 80, no. 14 (2021): 20929–70. http://dx.doi.org/10.1007/s11042-020-10198-9.
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AbstractPeer-to-peer streaming is a well-known technology for the large-scale distribution of real-time audio/video contents. Delay requirements are very strict in interactive real-time scenarios (such as synchronous distance learning), where playback lag should be of the order of seconds. Playback continuity is another key aspect in these cases: in presence of peer churning and network congestion, a peer-to-peer overlay should quickly rearrange connections among receiving nodes to avoid freezing phenomena that may compromise audio/video understanding. For this reason, we designed a QoS monitoring algorithm that quickly detects broken or congested links: each receiving node is able to independently decide whether it should switch to a secondary sending node, called “fallback node”. The architecture takes advantage of a multithreaded design based on lock-free data structures, which improve the performance by avoiding synchronization among threads. We will show the good responsiveness of the proposed approach on machines with different computational capabilities: measured times prove both departures of nodes and QoS degradations are promptly detected and clients can quickly restore a stream reception. According to PSNR and SSIM, two well-known full-reference video quality metrics, QoE remains acceptable on receiving nodes of our resilient overlay also in presence of swap procedures.
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Sevcik, Lukas, and Miroslav Voznak. "Adaptive Reservation of Network Resources According to Video Classification Scenes." Sensors 21, no. 6 (2021): 1949. http://dx.doi.org/10.3390/s21061949.
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Video quality evaluation needs a combined approach that includes subjective and objective metrics, testing, and monitoring of the network. This paper deals with the novel approach of mapping quality of service (QoS) to quality of experience (QoE) using QoE metrics to determine user satisfaction limits, and applying QoS tools to provide the minimum QoE expected by users. Our aim was to connect objective estimations of video quality with the subjective estimations. A comprehensive tool for the estimation of the subjective evaluation is proposed. This new idea is based on the evaluation and marking of video sequences using the sentinel flag derived from spatial information (SI) and temporal information (TI) in individual video frames. The authors of this paper created a video database for quality evaluation, and derived SI and TI from each video sequence for classifying the scenes. Video scenes from the database were evaluated by objective and subjective assessment. Based on the results, a new model for prediction of subjective quality is defined and presented in this paper. This quality is predicted using an artificial neural network based on the objective evaluation and the type of video sequences defined by qualitative parameters such as resolution, compression standard, and bitstream. Furthermore, the authors created an optimum mapping function to define the threshold for the variable bitrate setting based on the flag in the video, determining the type of scene in the proposed model. This function allows one to allocate a bitrate dynamically for a particular segment of the scene and maintains the desired quality. Our proposed model can help video service providers with the increasing the comfort of the end users. The variable bitstream ensures consistent video quality and customer satisfaction, while network resources are used effectively. The proposed model can also predict the appropriate bitrate based on the required quality of video sequences, defined using either objective or subjective assessment.
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Hussain, Md, and M. M. Beg. "Fog Computing for Internet of Things (IoT)-Aided Smart Grid Architectures." Big Data and Cognitive Computing 3, no. 1 (2019): 8. http://dx.doi.org/10.3390/bdcc3010008.
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The fast-paced development of power systems necessitates the smart grid (SG) to facilitate real-time control and monitoring with bidirectional communication and electricity flows. In order to meet the computational requirements for SG applications, cloud computing (CC) provides flexible resources and services shared in network, parallel processing, and omnipresent access. Even though CC model is considered to be efficient for SG, it fails to guarantee the Quality-of-Experience (QoE) requirements for the SG services, viz. latency, bandwidth, energy consumption, and network cost. Fog Computing (FC) extends CC by deploying localized computing and processing facilities into the edge of the network, offering location-awareness, low latency, and latency-sensitive analytics for mission critical requirements of SG applications. By deploying localized computing facilities at the premise of users, it pre-stores the cloud data and distributes to SG users with fast-rate local connections. In this paper, we first examine the current state of cloud based SG architectures and highlight the motivation(s) for adopting FC as a technology enabler for real-time SG analytics. We also present a three layer FC-based SG architecture, characterizing its features towards integrating massive number of Internet of Things (IoT) devices into future SG. We then propose a cost optimization model for FC that jointly investigates data consumer association, workload distribution, virtual machine placement and Quality-of-Service (QoS) constraints. The formulated model is a Mixed-Integer Nonlinear Programming (MINLP) problem which is solved using Modified Differential Evolution (MDE) algorithm. We evaluate the proposed framework on real world parameters and show that for a network with approximately 50% time critical applications, the overall service latency for FC is nearly half to that of cloud paradigm. We also observed that the FC lowers the aggregated power consumption of the generic CC model by more than 44%.
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Rozhon, Jan, Filip Rezac, Jakub Jalowiczor, and Ladislav Behan. "Augmenting Speech Quality Estimation in Software-Defined Networking Using Machine Learning Algorithms." Sensors 21, no. 10 (2021): 3477. http://dx.doi.org/10.3390/s21103477.
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With the increased number of Software-Defined Networking (SDN) installations, the data centers of large service providers are becoming more and more agile in terms of network performance efficiency and flexibility. While SDN is an active and obvious trend in a modern data center design, the implications and possibilities it carries for effective and efficient network management are not yet fully explored and utilized. With most of the modern Internet traffic consisting of multimedia services and media-rich content sharing, the quality of multimedia communications is at the center of attention of many companies and research groups. Since SDN-enabled switches have an inherent feature of monitoring the flow statistics in terms of packets and bytes transmitted/lost, these devices can be utilized to monitor the essential statistics of the multimedia communications, allowing the provider to act in case of network failing to deliver the required service quality. The internal packet processing in the SDN switch enables the SDN controller to fetch the statistical information of the particular packet flow using the PacketIn and Multipart messages. This information, if preprocessed properly, can be used to estimate higher layer interpretation of the link quality and thus allowing to relate the provided quality of service (QoS) to the quality of user experience (QoE). This article discusses the experimental setup that can be used to estimate the quality of speech communication based on the information provided by the SDN controller. To achieve higher accuracy of the result, latency characteristics are added based on the exploiting of the dummy packet injection into the packet stream and/or RTCP packet analysis. The results of the experiment show that this innovative approach calculates the statistics of each individual RTP stream, and thus, we obtain a method for dynamic measurement of speech quality, where when quality decreases, it is possible to respond quickly by changing routing at the network level for each individual call. To improve the quality of call measurements, a Convolutional Neural Network (CNN) was also implemented. This model is based on two standard approaches to measuring the speech quality: PESQ and E-model. However, unlike PESQ/POLQA, the CNN-based model can take delay into account, and unlike the E-model, the resulting accuracy is much higher.
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"Enhancing QOS/QOE in MANETs using OLSR Protocol." International Journal of Innovative Technology and Exploring Engineering 8, no. 12 (2019): 347–54. http://dx.doi.org/10.35940/ijitee.l3249.1081219.
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Video streaming over Mobile Ad-hoc Networks (MANETs) has been on the fore as one of the most chiefly solicited web services. Within this context, the enhancement of the Quality of Experience (QoE) on such platforms, whose signature characteristic is the real-time fluctuation of their Mobile Nodes (MNs), remains a high-stakes challenge for highfidelity transmission via the extant MANET routing protocols. Indeed, the free mobility of MNs (i.e. their real-time physical reallocation, viz. intra-network movement), renders network topology often subject to unpredictable fluctuations. It is this margin of relative unpredictability which lends itself to such instances where QoE, as perceived by the Customer/User, may be subject to varying degrees of depreciation.In this perspective, our contribution in this paper aims to optimize the MANETs' mobility system, through the re-adaptation of some extant MANET routing protocols, so as to afford safer routing courses (lower mobility thresholds equate to lower chances of noise and/or data corruption/loss); all for the capital purpose of improving subjective quality (i.e. the anticipated end-user's personal assessment of the service).For the implementation of our MANET network, our two-fold choice consisted of the NS2 version 2.9 (a powerful platform with highly reliable protocol support), supplemented by the Evalvid Framework (a fieldproven tool according to many expert ratings, ideal for close monitoring of QoE metrics).We have considered various video transmission scenarios through the OLSR protocol, one of the most well-known and reliable proactive MANET routing protocols .As for QoE prediction, we expect the mean opinion scores (MOS) to provide a metric template for a rough estimation of the end-users' anticipated appraisal .The results are to demonstrate, as we shall see, that the modified heuristic algorithm of OLSR, leaning on the underlying criterion of mobility, can lead to a significant performance boost in MANETs and, by the same token, to higher returns of QoS and QoE
Dissertations / Theses on the topic "QoE, QoS, Monitoring, Videoqualität"
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Knoll, Thomas Martin, and Marcus Eckert. "Improvement of network-based QoE estimation for TCP based streaming services." Universitätsbibliothek Chemnitz, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-147667.
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Progressive download video services, such as YouTube and podcasts, are responsible for a major part of the transmitted data volume in the Internet and it is expected, that they will also strongly affect mobile networks. Streaming video quality mainly depends on the sustainable throughput achieved during transmission. To ensure acceptable video quality in mobile networks (with limited capacity resources) the perceived quality by the customer (QoE) needs to be monitored by estimation. For that, the streaming video quality needs to be measured and monitored permanently. For TCP based progressive download we propose to extract the the video timestamps which are encoded within the payload of the TCP segments by decoding the video within the payload. The actual estimation is then done by play out buffer fill level calculations based on the TCP segment timestamp and their internal play out timestamp. The perceived quality for the user is derived from the number and duration of video stalls. Algorithms for decoding Flash Video, MP4 and WebM Video have already been implemented. After deriving the play out time it is compared to the timestamp of the respective TCP segment. The result of this comparison is an estimate of the fill level of the play out buffer in terms of play out time within the client. This estimation is done without access to the end device. The same measurement procedure can be applied for any TCP based progressive download Internet service. Video was simply taken as an example because of its current large share in traffic volume in operator networks.
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Knoll, Thomas Martin, and Marcus Eckert. "Demo on Network-based QoE measurement for Video streaming services." Universitätsbibliothek Chemnitz, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-147676.
Full textAbstract:
Progressive download video services, such as YouTube, are responsible for a major part of the transmitted data volume in the Internet and it is expected, that they also will strongly affect mobile networks. Streaming video quality mainly depends on the sustainable throughput achieved during transmission. In order to achieve an acceptable video quality in mobile networks (with limited capacity resources), traffic engineering mechanisms have to be applied. For that, the streaming video quality needs to be measured and monitored permanently. Therefore, the video timestamps which are encoded within the payload of the TCP segments have to be extracted. For that it is necessary to decode the video within the transported payload. Algorithms for decoding Flash Video, MP4 and WebM Video have already been implemented as a demonstration implementation in support of the network based measurement contribution to SG12 by Chemnitz University for TCP encoded progressive download Internet services. In the demonstration, the derived play out buffering from the monitored traffic is being output internally. A second application is then used to graphically display the estimation result. The measurement and estimation is solely done within a measurement point of an operator network without access to the client’s end device.
Books on the topic "QoE, QoS, Monitoring, Videoqualität"
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Soldani, David. Qos And Qoe Management in Umts Mobile Networks: An Introduction to Service Planning, Provisioning, Performance Monitoring And Optimisation. John Wiley & Sons Inc, 2006.
Find full textBook chapters on the topic "QoE, QoS, Monitoring, Videoqualität"
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Soldani, David, Davide Chiavelli, Jaana Laiho, et al. "QoE and QoS Monitoring." In QoS and QoE Management in UMTS Cellular Systems. John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/9780470034057.ch9.
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Skorin-Kapov, Lea, Ognjen Dobrijevic, and Domagoj Piplica. "Towards Evaluating the Quality of Experience of Remote Patient Monitoring Services." In Healthcare Ethics and Training. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2237-9.ch056.
Full textAbstract:
The applicability of advanced mobile technologies in the m-Health domain has led to a number of studies and (limited) commercial products supporting delivery of health services to remote users. A key issue regarding successful delivery and acceptance of such services is meeting their Quality of Service (QoS) and Quality of Experience (QoE) requirements, focusing on technical aspects and end user perceived quality, respectively. In this paper, the authors address the topic of evaluating QoE for non-emergency remote patient monitoring services. They identify relevant QoE influence factors and metrics, and present the results of a QoE evaluation study, whereby they focus on usability aspects. The study involves 26 users testing a prototype version of the Ericsson Mobile Health service, which is based on a smartphone application and measurement of vital signs via medical sensors. The results show a strong correlation between QoE and: perceived effectiveness of the mobile interface (regarding both adequacy of smartphone screen size and smartphone application navigational support), perceived ease of conducting a blood pressure measurement task, and user motivation for service usage.
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Huang, Pingguo, and Yutaka Ishibashi. "QoS Control in Remote Robot Operation with Force Feedback." In Robotics Software Design and Engineering. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.97011.
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Recently, many researchers focus on studies of remote robot operation with force feedback. By using force feedback, since users can touch remote objects and feel the shape, weight, and softness of each object, the efficiency and accuracy of operation can be largely improved. However, when the haptic information such as force and/or position information is transmitted over a QoS (Quality of Service) non-guaranteed network like the Internet, QoE (Quality of Experience) and stability may seriously deteriorate. Therefore, it is important to carry out QoS control and stabilization control together to solve the problems. In this chapter, we mainly focus on QoS control. We also introduce our remote robot system with force feedback which we constructed to study QoS control and stabilization control by experiment. In the system, a user operates a remote industrial robot with a force sensor by using a local haptic interface device while monitoring the robot operation by a video camera. We handle two types of operation; operation with a single remote robot system and that between two remote robot systems. We explain several types of QoS control which we have proposed so far for remote robot operation with force feedback. Finally, we discuss the challenges and future directions of QoS control in remote robot operation with force feedback.
Conference papers on the topic "QoE, QoS, Monitoring, Videoqualität"
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Seungho Jeong and Heejune Ahn. "Mobile IPTV QoS/QoE monitoring system based on OMA DM protocol." In 2010 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2010. http://dx.doi.org/10.1109/ictc.2010.5674715.
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Rozhon, Jan, Filip Rezac, Jakub Safarik, Erik Gresak, and Jakub Jalowiczor. "Measuring and monitoring the QoS and QoE in software defined networking environments." In Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII, edited by Lynne L. Grewe, Erik P. Blasch, and Ivan Kadar. SPIE, 2019. http://dx.doi.org/10.1117/12.2518838.
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Galetto, S., P. Bottaro, C. Carrara, et al. "Detection of video/audio streaming packet flows for non-intrusive QoS/QoE monitoring." In 2017 IEEE International Workshop on Measurements & Networking (M&N). IEEE, 2017. http://dx.doi.org/10.1109/iwmn.2017.8078403.
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Huntgeburth, Bastian, Michael Maruschke, and Sebastian Schumann. "Open-Source Based Prototype for Quality of Service (QoS) Monitoring and Quality of Experience (QoE) Estimation in Telecommunication Environments." In 2011 5th International Conference on Next Generation Mobile Applications, Services and Technologies (NGMAST). IEEE, 2011. http://dx.doi.org/10.1109/ngmast.2011.37.
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