End-user Quality Of Experience Research Articles

Real-time video freezing detection for 4K UHD videos

Video frame freezing is a common artifact which can occur during video content delivery due to errors in the video coding process, video transmission, storage or reproduction. This artifact can significantly decrease the end-user Quality of Experience. Therefore, accurate video frame freezing detection is of great importance for different parties involved in video content delivery. In this paper, a new Real-Time no-reference Freezing Detection Algorithm, called the RTFDA, is proposed. As newly video frames are acquired, the RTFDA performs comparison of the current video frame with the corresponding previous one to detect whether video freezing is occurring. The comparison is made by calculating the corresponding subsampled video frames and their pixel-by-pixel absolute difference comparison. The benefit of such approach is twofold: the influence of noise in freezing frames on frame comparison is significantly reduced as well as computational complexity of frame comparison. The RTFDA has a high detection rate with a very low rate of both false-positive and false-negative detections, outperforming four freezing detection algorithms on four different video databases. The proposed implementation on an x86-64 platform achieves real-time performance on 4K Ultra High Definition (UHD) videos by processing 216 frames per second (fps). Apart from that, FPGA implementation is proposed, which has efficient FPGA resource utilization.

Real-time no-reference histogram-based freezing artifact detection algorithm for UHD videos

During video transmission different errors can occur, which can introduce distinct artifacts in video received at the end user side. One of the most common artifacts that appears in such cases is frame freezing and it can significantly reduce the quality of received video, i.e. decrease the level of end user Quality of Experience (QoE). Thus it is necessary to properly detect the occurrence of frame freezing, in order to assure the satisfactory level of end user QoE. In this paper a novel no-reference (NR) objective algorithm for detection of various types of freezing artifacts in video, called Histogram-Based Freezing artifacts Detection Algorithm (HBFDA), is proposed. HBFDA uses method for comparison of consecutive video frames, which consists of splitting frame into regions and comparing regions’ histograms of consecutive frames. In order to operate on different types of video contents (including those containing a low level of movement), while achieving high level of accuracy and reliability, HBFDA dynamically adapts its parameters in real-time. HBFDA performance are compared to this of two freely pulbicly available algorithms for frame freezing detection on videos from three different databases: VQEG HDTV, LIVE Mobile and newly created UHD Video Freezing Database (UHD VFD), which we made publicly available to the research community and it can be downloaded at http://www.rt-rk.com/other/UHDVideoDBReadme.html . Experimental results show that HBFDA has lowest number of false freezing detections when compared to other tested algorithms and that it achieves the highest average total accuracy of 99.26% when all databases are taken into account. Additionally, for UHD videos, HBFDA is able to process 75 frames per second, which makes it suitable for real-time video applications.

Joint Optimization on Bandwidth Allocation and Route Selection in QoE-Aware Traffic Engineering

Wide area networks (WANs) are increasingly relying on software-defined networking to orchestrate data transmission. Google and Microsoft built B4 and SWAN, respectively, to interconnect their data centers in WANs, achieving higher network utilization and lower delay, where different priorities can be applied for bandwidth allocation and route selection in their centralized traffic engineering. However, simply relying on the priorities cannot accurately capture the complex properties of the quality of experience (QoE) function corresponding to the end users and always result in a suboptimal solution. We propose QTE, a novel traffic engineering in software-defined WANs, which proactively enforces forwarding policies by coordinating the traffic demand of each data center. The goal of QTE aims to maximize end-user QoE for client-trigger traffic and maximize network throughput for background traffic. We make several technical contributions in designing QTE. First, we formulate maximizing end-user QoE problem as two optimization programs and propose a heuristic algorithm to solve the problem. We further present a concrete design and implementation of the system based on the Floodlight controller. Extensive experiments use Openflow switches on Mininet and numerical simulations, which shows that QTE increases end-user experiences by 45.3% compared with prior work and can be readily implemented on the Floodlight controller.

Impact of Video File Format on Quality of Experience (QoE) of Multimedia Content

Video file formats are used to record, play and store videos according to heterogeneous platforms; devices and online streaming on networks vary by speed. Video formats are different codecs and resolution from each other and also vary in display quality and storage size which also differentiate the quality of experience (QoE) of users. This paper presents the subjective QoE of end user’s for different video file format varied according to resolution quality and bitrate. Videos of most popular formats which are used for online streaming was downloaded from YouTube and stored on the local system for play for subjects to perceive the video quality. Subjective QoE experiments were conducted by questionnaires given to subjects to perceive video quality and assign ratings. Results show high ratings for FLV 240P and WebM 360P for low resolution videos where high ratings for the MP4 video for 720P and 2160P high resolutions.

Measuring Effect of Packet Reordering on Quality of Experience (QoE) in Video Streaming

Video streaming is an important part of the Internet and it is widely used for IPTV, video conferencing and E-learning. The quality of experience (QoE) of users about the video contents is important for service providers to improve services and provide quality of service to end users. The video quality is affected by packet reordering, delay and loss in the network, which degrades the QoE of end users. In this paper, we conducted several experiments on video streaming creating artificial packet reorder to measure user stratification level of video quality as well as acceptable network level for end users. We used NetEm tool for creating artificial packet reorder in video streaming, recording and playing videos for participants for perception, and investigated the QoE of different packet reorder percentage. From our experiments, we found that QoE of users is decreased when video quality is reordered due to network traffic. This work will help content providers to provide QoE to users based on service level agreements.

Cloud Computing: Secure Transmission of High Definition Videos Using Cryptographic Approach

Cloud computing facilitates ubiquitous access to shared pool of configurable system resources and services over the Internet. Often due to shared access to this massive amount of data there is equal chances of risk. Transferring sensitive information in the form of text, audio or video, over the cloud one cannot guarantee the safety of the file. This paper assesses the effect of transmission of High Definition videos with the help of cloud-based servers that will improve the security of data being transmitted as well as enhance the quality of experience for end-users. We propose to share video contents to a selective group of people using the Time Domain Attribute based Access Control(TAAC) schema and generate keys using cryptographic method which gives the much needed protection to access these videos. Further stegnographic approaches are practised to maintain the data confidentiality.

E3DOAS: Balancing QoE and Energy-Saving for Multi-Device Adaptation in Future Mobile Wireless Video Delivery

Smart devices (e.g., smartphones, tablets, smart-home devices, etc.) have become important companions to most people in their daily activities, and are very much used for multimedia content exchange (i.e., video sharing, real-time/non-real-time multimedia streaming), contributing to the exponential increase in mobile traffic over the current wireless networks. While the next generation mobile networks will provide higher capacity than the current 4G systems, the network operators will face important challenges associated with the outstanding increase of both video traffic and user expectations in terms of their levels of perceived quality or quality of experience (QoE). Furthermore, the heterogeneity of mobile devices (e.g., screen resolution, battery life, and hardware performance) also impacts severely the end-user QoE. In this context, this paper proposes an evolved QoE-aware energy-saving device-oriented adaptive scheme (E3DOAS) for mobile multimedia delivery over future wireless networks. E3DOAS makes use of a coalition game-based rate allocation strategy within the multi-device heterogeneous environment, and optimizes the trade-off between the end-user perceived quality of the multimedia delivery and the mobile device energy-saving. Testing has involved a prototype of E3DOAS, a crowd-sourcing-based QoE assessment method to model non-reference perceptual video quality, and an energy measurement testbed introduced to collect power consumption parameters of the mobile devices. Simulation-based performance evaluation showed how E3DOAS outperformed other state of the art multimedia adaptive solutions in terms of energy saving, end-to-end quality of service metrics and end-user perceived quality.

Smart resource allocation for improving QoE in IP Multimedia Subsystems

IP Multimedia Subsystem (IMS) is a robust multimedia service. IMS becomes more important when delivering multimedia services. Multimedia service providers can benefit from IMS to ensure a good QoE (Quality of Experience) to their customers with minimal resources usage. In this paper, we propose an intelligent media distribution IMS system architecture for delivering video streaming. The system is based primarily on uploading a multimedia file to a server in the IMS. Later, other users can download the uploaded multimedia file from the IMS. In the system, we also provide the design of the heuristic decision methods and models based on probability distributions. Thus, our system takes into account the network parameters such as bandwidth, jitter, delay and packet loss that influence the QoE of the end-users. Moreover, we have considered the other parameters of the energy consumption such as CPU, RAM, temperature and number connected users that impact the result of the QoE. All these parameters are considered as input to our proposal management system. The measurements taken from the real test bench show the real performance and demonstrate the success of the system about ensuring the upload speed of the multimedia file, guaranteeing the QoE of end users and improving the energy efficiency of the IMS.

Trade-off in optimizing energy consumption and end user quality of experience in radio access network

To reduce network access latency, network traffic volume and server load, caching capacity has been proposed as a component of evolved Node B (eNodeB) in the ratio access network (RAN). These eNodeB caches reduce transport energy consumption but lead to additional energy cost by equipping every eNodeB with caching capacity. Existing researches focus on how to minimize total energy consumption, but often ignore the trade-off between energy efficiency and end user quality of experience, which may lead to undesired network performance degradation. In this paper, for the first time, we build an energy model to formulate the problem of minimizing total energy consumption at eNodeB caches by taking a trade-off between energy efficiency and end user quality of experience. Through coordinating all the eNodeB caches in the same RAN, the proposed model can take a good balance between caching energy and transport energy consumption while also guarantee end user quality of experience. The experimental results demonstrate the effectiveness of the proposed model. Compared with the existing works, our proposal significantly reduces the energy consumption by approximately 17% while keeps superior end user quality of experience performance.

Efficient caching resource allocation for network slicing in 5G core network

Network slicing has been considered as one of the key technologies in the next generation mobile network (fifth generation – 5G), which can create virtual network and provide customised services on demand. Most of the current work on network slicing mainly focuses on virtualisation technology, especially in virtual resource allocation. However, caching as a significant approach to improve the content delivery and quality of experience for end-users has not been well considered in network slicing. In this study, the authors consider in-network caching combining with network slicing, and propose an efficient caching resource allocation scheme for network slicing in 5G core network. They first formulate the caching resource allocation issue as an integer linear programming model, and then propose a caching resource allocation scheme based on chemical reaction optimisation (CRO) algorithm, which can significantly improve the caching resource utilisation. The CRO algorithm is a population-based optimisation metaheuristic, which has advantages in searching optimal solution and computation complexity. Finally, extensive simulation results are presented to illustrate the performance of the proposed scheme.

Improving QoE and Fairness in HTTP Adaptive Streaming Over LTE Network

HTTP adaptive streaming (HAS) has emerged as the main technology for video streaming applications. Multiple HAS video clients sharing the same wireless channel may experience different video qualities as well as different play-out buffer levels, as a result of both different video content complexities and different channel conditions. This causes unfairness in the end-user quality of experience. In this paper, we propose a quality-fair adaptive streaming solution with fair buffer (QFAS-FB) to deliver fair video quality and to achieve asymptotically fair play-out buffer levels among HAS clients competing for the same wireless resources in a long-term evolution (LTE) cell. In the QFAS-FB framework, the share of radio resources is optimized according to video content characteristics, play-out buffer levels, and channel conditions. The proposed solution is compared with the other state-of-the-art strategies, and the numerical results show that it significantly improves the quality fairness among heterogeneous HAS users, reduces the video quality variations, and improves the fairness among the user’s play-out buffers.

An adaptive type-2 fuzzy traffic engineering method for video surveillance systems over software defined networks

Software Defined Network (SDN) is a new network technology which allows network providers to afford predefined Quality of Service (QoS) for video streaming applications. Network administrators can develop desired traffic engineering techniques over SDN and support Quality of Experience (QoE) and QoS for their customers. One of the most important issues in traffic engineering is to find favorable links for routing between source and destination. The fitness of each link in the network depends on the end users QoE and the applications they are used. In this paper to achieve optimal routes, the fitness of each link is determined by type-2 fuzzy sets. Then, an adaptive traffic engineering method is proposed to find the best routes between source cameras and monitoring center in a video surveillance system. The proposed method is based on Constraint Shortest Path (CSP) problem and calculates minimum cost path which satisfies delay constraint. Due to NP-completeness of the CSP problems, LARAC algorithm is used to solve it. To the best of our knowledge, this is the first proposed traffic engineering technique which is based on type-2 fuzzy set for video streaming applications over SDN. The contribution of the proposed method regarding to the related works, is to apply type-2 and type-1 fuzzy logic for calculating the costs of network links based on QoE for providing QoS in a video surveillance system. In addition, this paper models the provisioning of QoS in a real scenario and emulates them in a network emulator. Many comparisons carried out between the proposed method and other well-known methods to show the effectiveness of the proposed method in terms of packet loss, delay and PSNR.

Predicting telemedicine system user satisfaction in sub-Saharan Africa

Abstract During our literature review and on-site research in developing countries, particularly in Sub-Saharan Africa, we could not point out any efforts to measure the Quality of Service (QoS) and Quality of Experience (QoE) provided by telemedicine systems, thus making it impossible to understand their overall levels of user satisfaction in sub-Saharan African countries. Therefore, we conducted a qualitative and quantitative study, with experiments to measure QoS performance and the end-user QoE perception of telemedicine systems. Based on obtained results, we propose a mathematical formula to predict the QoE based on QoS measurement values, since a known relationship exists between QoS and QoE.

A QoE-Aware Resource Distribution Framework Incentivizing Context Sharing and Moderate Competition

We contend that context information of Internet clients can help to efficiently manage a variety of underlying resources for different Internet services and systems. We therefore propose a resource distribution framework that provides quality of experience (QoE) aware service differentiation, which means that starving clients are prioritized in resource allocation to enhance the corresponding end-user's QoE. The framework also actively motivates each Internet client to consistently provide its actual context information and to adopt moderate competition policies, given that all clients are selfish but rational in nature. We analyze the Internet client's behavior by formulating a non-cooperative game and prove that the framework guides all clients (game players) towards a unique Nash equilibrium. Furthermore, we prove that the distribution results computed by the framework maximize a social welfare function. Throughout this paper, we demonstrate the motivation, operation and performance of the framework by presenting a Web system example, which leverages on the advanced context information deduced by a context-aware system.

Profit Optimization for Wireless Video Broadcasting Systems Based on Polymatroidal Analysis

This study addresses the problem of profit maximization between wireless service providers (WSPs) and content providers (CPs) in wireless broadcasting systems , while simultaneously providing high quality of experience for end-users (EUs). We first study the profit model in wireless broadcasting networks with a particular attention to the heterogeneous requirements of EUs, e.g., different display sizes and variable channel conditions. Then, we propose a profit formulation that describes the requirements of wireless service providers and content providers, as well as the satisfaction of EUs that essentially depends on video quality and service charges. We propose a new polymatroidal theoretic framework for maximizing the resulting three-side achievable profit through proper bandwidth allocation. Our framework exploits two particular structures, namely the underlying polymatroidal structure of the profit region and the contra- polymatroidal structure of the rate region. We then propose a profit maximization solution by finding a rate allocation vector on the sum-rate facet that satisfies the maximal achievable profit among the WSP, CPs, and EUs. Experiments on different broadcasting scenarios demonstrate the effectiveness of the proposed method. The WSP is capable of generating more revenues by applying the proposed approach to their marketing strategies while satisfying the demands from CPs and EUs.

Shielding video streaming against packet losses over VANETs

Vehicular ad-hoc networks (VANETs) are being widely adopted in the last few years. This type of network enables the utilization of a large diversity of distributed applications, such as road and traffic alerts, autonomous driving capabilities and video distribution. Video applications can be considered one of the most demanding services because it needs a steady and continuous flow of information. This presents a set of challenges to VANETs considering their scarce network resources due to the vehicle movement and time-varying wireless channels. Considering the above mentioned issues, an adaptive quality of experience (QoE)-driven mechanism is needed to provide live transmission capabilities to video-equipped vehicles. This mechanism has to overcome the challenges to grant a high-qua lity video transmission without adding any unnecessary network overhead. To this end, a forward error correction (FEC) technique can be adapted to enhance the video distribution, leading to higher QoE for end users. The proposed self-adaptive FEC-based mechanism (SHIELD) uses several video characteristics and specific VANETs details to safeguard real-time video streams against packet losses. One of the main contributions of this work is the combined used of network density, signal-to-noise ratio, packet loss rate, and the vehicle's position. This allows SHIELD to better protect the video sequences and enhance the QoE. In doing that, we are able to improve the user experience, while saving network resources. The advantages and drawbacks of the proposed mechanism are demonstrated through extensive experiments and assessed with QoE metrics, proving that it outperforms both adaptive and non-adaptive mechanisms.

Parallel TCP and scalable video coding for jitter free video transmission over MIMO wireless networks

There is a significant rise in demand for video transmission over 3G and 4G wireless networks due to the rising popularity of video streaming websites such as YouTube. The market for video streaming over wireless networks is expected to increase sharply in the future. Both of the two basic transport layer protocols without modifications are not suited for video transmission over wireless networks. UDP (user datagram protocol) suffers from inherent unreliability, resulting in corrupted video due to frequent corruption of packets. Inherent features of wireless networks such as noise, interference, etc. result in packet corruption. On the other hand, the performance of TCP (transmission control protocol) is worse than UDP (Thangaraj et al. in Telecommun Syst 45(4):303---312, 2010) because of frequently corrupted packets. Due to its reliable data transfer feature, TCP continuously retransmits the corrupted packet until successful reception at the receiver. This leads to jitter in video playback and poor end user quality of experience. Multiple TCP connections with appropriate optimization can lead to an increased efficiency of bandwidth utilization in comparison to single TCP based video transmission over wireless networks. It has been shown that multiple TCP connections enhance the video transmission and playback experience by providing reliable communication. The parallel TCP scheme proposed in this paper enhances the quality of video transmission and playback experience over MIMO wireless networks employing scalable hierarchical wavelet decomposition based video encoding with multiple TCP connections.

Statistical user behavior detection and QoE evaluation for thin client services

Remote desktop connection (RDC) services offer clients the ability to access remote content and services, commonly in the context of accessing their working environment. With the advent of cloud-based services, an example use case is that of delivering virtual PCs to users in WAN environments. In this paper, we aim to detect and analyze common user behavior when accessing RDC services, and use this as input for making Quality of Experience (QoE) estimations and subsequently providing input for effective QoE management mechanisms. We first identify different behavioral categories, and conduct traffic analysis to determine a feature set to be used for classification purposes. We propose a machine learning approach to be used for classifying behavior, and use this approach to classify a large number of real-world RDCs. We further conduct QoE evaluation studies to determine the relationship between different network conditions and subjective end user QoE for all identified behavioral categories. Results show an exponential relationship between QoE and delay and loss degradations, and a logarithmic relationship between QoE and bandwidth limitations. Obtained results may be applied in the context of network resource planning, as well as in making QoE-driven resource allocation decisions.

When YouTube Does not Work—Analysis of QoE-Relevant Degradation in Google CDN Traffic

YouTube is the most popular service in today's Internet. Google relies on its massive content delivery network (CDN) to push YouTube videos as close as possible to the end-users, both to improve their watching experience as well as to reduce the load on the core of the network, using dynamic server selection strategies. However, we show that such a dynamic approach can actually have negative effects on the end-user quality of experience (QoE). Through the comprehensive analysis of one month of YouTube flow traces collected at the network of a large European ISP, we report a real case study in which YouTube QoE-relevant degradation affecting a large number of users occurs as a result of Google's server selection strategies. We present an iterative and structured process to detect, characterize, and diagnose QoE-relevant anomalies in CDN distributed services such as YouTube. The overall process uses statistical analysis methodologies to unveil the root causes behind automatically detected problems linked to the dynamics of CDNs' server selection strategies.

QoE assessment of interactive applications in computer networks

Measuring end user Quality of Experience (QoE) is currently performed by subjective or objective standard methods each with its own deficiencies. The subjective quality assessment is laboratory based, costly and offline; while, the objective estimation of user satisfaction is obtained through a static manner, not directly related to end user contentment. The attempt is made here to measure user QoE based on an online, user-aware and non-intrusive method. This is investigated by identifying the measurable objective indicators of user satisfaction/dissatisfaction and assigning them to the subjective nature of QoE concept. Proposing an architectural model, the extent of modalities for implicit sensing of the user QoE is explored with respect to the real-time measurement of her/his experiences. The vocal and interactional signs of VoIP service users on their smartphone devices are applied to estimate their satisfaction/dissatisfaction levels as a case study. The obtained results are compared to the users' self-report in order to evaluate the accuracy of this proposed method.