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Within the current global context, the coronavirus pandemic has led to an unprecedented surge in the Internet traffic, with most of the traffic represented by video. The improved wired and guided network infrastructure along with the emerging 5G networks enables the provisioning of increased bandwidth support while the virtualization introduced by the integration of Software Defined Networks (SDN) enables traffic management and remote orchestration of networking devices. However, the popularity and variety of multimediarich applications along with the increased number of users has led to an ever increasing pressure that these multimedia-rich content applications are placing on the underlying networks.\ud \ud Consequently, a simple increase in the system capacity will not be enough and an intelligent traffic management solution is required to enable the Quality of Service (QoS) provisioning. In this context, this paper proposes a Reinforcement Learning (RL)-based framework within a multimedia-based SDN environment, that decides on the most suitable routing algorithm to be applied on the QoS-based traffic flows to improve QoS provisioning. The proposed RL-based solution was implemented and evaluated using an experimental setup under a realistic SDN environment and compared against other state-of-the-art solutions from the literature in terms of throughput, packet loss, latency, peak signal-to-noise ratio (PSNR) and mean opinion score (MOS). The proposed RL-based framework finds the best trade-off between QoS vs. Quality of User Experience (QoE) when compared to other state-of-the-art approaches.

In this paper, we address the issues of the trade-off between QoS and QoE with an analytical analysis based on mathematical modeling under a unified normalization measurement. We model through computation the awareness of QoS and QoE with a strategy of quality-aware QoE-QoS coordination. A balanced coordination is proposed using modeling correlations between user experience and service performance. The main contributions of this paper include three main parts. First, a comprehensive mapping is modeled in a close form to illustrate the analytic correlations between QoS, QoE, and data communication. Second, an analytical method to analyze and coordinate the nonlinear trade-off between QoE and QoS is proposed based on the theoretical proof with discussions on necessary-sufficient conditions. Third, an algorithmic framework is provided to perform QoE-QoS coordination based on quality-awareness computing with a test proof. An assessment model for user experience quantification is built with the mean opinion score (MOS) test. Quality-aware QoE and QoS models are built based on the subspace learning strategy. Simulations are given to prove the feasibility and effectiveness of the proposed method. The results show that the operations with the proposed solution can be obtained analytically with balanced efficiency in both user experience performance and network performance.

Nowadays, heterogeneous devices are widely utilizing Hypertext Transfer Protocol (HTTP) to transfer the data. Furthermore, HTTP adaptive video streaming (HAS) technology transmits the video data over wired and wireless networks. In adaptive technology services, a client’s application receives a streaming video through the adaptation of its quality to the network condition. However, such a technology has increased the demand for Quality of Experience (QoE) in terms of prediction and assessment. It can also cause a challenging behavior regarding subjective and objective QoE evaluations of HTTP adaptive video over time since each Quality of Service (QoS) parameter affects the QoE of end-users separately. This paper introduces a methodology design for the evaluation of subjective QoE in adaptive video streaming over wireless networks. Besides, some parameters are considered such as video characteristics, segment length, initial delay, switch strategy, stalls, as well as QoS parameters. The experiment’s evaluation demonstrated that objective metrics can be mapped to the most significant subjective parameters for user’s experience. The automated model could function to demonstrate the importance of correlation for network behaviors’ parameters. Consequently, it directly influences the satisfaction of the end-user’s perceptual quality. In comparison with other recent related works, the model provided a positive Pearson Correlation value. Simulated results give a better performance between objective Structural Similarity (SSIM) and subjective Mean Opinion Score (MOS) evaluation metrics for all video test samples.

Network applications demand quality of service for enhanced call quality and increased user satisfaction. Hence, implementing queuing discipline at switches and routers in a network will govern the way packets are buffered while waiting for transmission. Queuing disciplines like first-in first-out (FIFO) queuing, priority queuing (PQ), weighted-fair queuing (WFQ), custom queuing and modified weighted round robin are more prominently deployed in network applications. These queuing disciplines help to control and manage network resources by fixing priorities for specific types of data on the network. The paper aims to analyze the quality of service parameters for various real time applications like File transfer protocol(FTP), email, database, Voice over IP(VOIP), video and web browsing,etc., and helps to optimise the use of available network resources. Moreover the quality of the network is compared using different QoS parameters such as end-to-end delay, throughput, jitter and Mean Opinion Score (MOS). This paper focuses on various queuing disciplines with low and high network traffic.

This paper examines influences of the network latency on cooperative work in a networked virtual environment with haptics by QoE (Quality of Experience) subjective and objective assessment. As the work, two users cooperatively carry an object in a networked maze system by using haptic interface devices. Assessment results demonstrate that as the network latency becomes longer, MOS (Mean Opinion Score) as a subjective measure becomes higher. We also clarify the relations between MOS and several objective measures by regression analysis.

In literatures, many works on the area of massive MIMO (Multi Input Multi Output) presented the improvement of signal quality by using precoding in accordance with partial structure. However, only signal quality is not good enough to represent the satisfaction of users. In this paper, we proposed the precoding for partially-structured massive MIMO by utilizing user satisfaction in term of Quality of Experience (QoE). The level of Mean Opinion Score (MOS) is adopted to express QoE of web browsing service. In the first step, we optimize the baseband precoding and RF precoding by proposing the alternating minimization methods, then optimize the QoS parameters for the best QoE. The results show that the optimized QoS parameters lead to the satisfaction of user and the achieving of the required level of QoE.

This system designed by website-based with service quality method which providing display online questionnaire that can be access by students of PSTI Unram. Students objectively fill the questionnaire, then the result will be displayed on page of Head Master, Lecturer and Operator PSTI Unram. There three ways in testing the application, Black Box, System, and Mean Opinion Score. Black Box shows that all the features run well. System shows the result of the system servqual score calculation is accordance with manual calculation. MOS administred 40 respondence of PSTI Unram Students, the result as much as 4,15. This shows that the system has been done well and accordance with the expected. Concluded that with this system, studens able to give the evaluation towards service provided during teaching and learning process, and PSTI Unram can receive the information about the analysis result of students satisfaction level towards the quality of service given. And for PSTI Unram can improve performance to provide an excellent services in the future.

The upcoming 5G cellular wireless network brings new challenges and problematic issues in providing services with different quality of service (QoS) requirements and serving huge amount of mobile devices in a spectrum-efficient manner. 5G-based systems will combine macrocells, different type of small cells and heterogeneous networks. As a result of this combination, a 5G-based network will feature a sophisticated multi-layered architecture, and a proper resource allocation will become a major challenge for it. A reliable provision of services as well. In this paper, the analysis of the impact of different QoS schedule algorithms (Round Robin, Best CQI and PF) to the allocation of resources and a reliability of data transmission in 5G network was carried out. Also, the relation of QoS characteristics (BER, data loss) to the perceptual evaluation of service quality by the end user in different ways of the resource allocation on 5G network was investigated also. The perceptual evaluation of a service quality, known as Quality of Experience, was investigated using mean opinion score method.

The world is directly moving from IPv4 to IPv6 due to some gaps in IPv4 such as address space exhaustion, security issues, mobility, and quality of service. These gaps gave birth to the development of a new version of the Internet Protocol named IPv6. This version of the protocol offers many improvements, including an increase in the address space from 232 to 2128 and improvements in security, mobility, and quality of service. In this article, we study the performance of these protocols on different applications, namely HTTP, FTP, database, email, and VoIP using OPNET Modeler as a simulation tool. For this, we designed a network, which includes different components such as servers, routers, and adapters. The performance measurement criteria are (i) the response time, (ii) the received traffic, (iii) the delay, (iv) the jitter, and (v) the MOS score. The aim of this work is to discover what is the best IP for a particular application in relation to the kind of traffic (heavy or light).

Due to COVID-19, many people throughout the world have had to suddenly transition to working and studying from home. This has led to an increase in data traffic in the home WiFi networks using the 802.11ac or 802.11n routers. In this paper, we have investigated the performance of the home WiFi networks in supporting the quality-of-service (QoS) needs of multiple home users concurrently watching streaming videos, playing online games, participating in online meetings, downloading huge files, etc. We measured the performance of these traffic streams using metrics such as delay, throughput, MOS-LQO (Mean Opinion Score - Listening Quality Objective) metric for audio streams and the VMAF (Video Multimethod Assessment Fusion) metric for video streams. Our results indicated that under highly congested network conditions, the 802.11ac router’s default settings performs the best in supporting the multiple concurrent traffic streams. In particular, our results show that the WMM 802.11e QoS feature in the router is critical to the router’s ability to prioritize video and audio traffic over other normal data traffic, and hence should always be enabled. Our results also show that the 802.11n router should be configured to operate in the 5 GHz frequency band. This is to avoid the congested 2.4 GHz frequency band and therefore enable the 802.11n router to support the QoS needs of the home users.

The development of immersive media systems and services relies on subjective tests that provide ground truth on the quality of experience as perceived by humans. In this paper, we investigate the opinion score consistency through a repeated subjective 360° video quality assessment experiment. The test stimuli are presented on a head-mounted display with participants given the task to rate the video quality in three repeated sessions for both standing and seated viewing. The statistical analysis of the data from the subjective tests aims at revealing if participants change their rating behavior over time or keep their opinion scores given to the test stimuli in standing and seated viewing consistent in a statistical sense. The experimental results are reported in terms of histograms of opinion scores, skewness and kurtosis of opinion scores, mean opinion scores, standard deviation of opinion scores (SOS), SOS fitting functions, and analysis of variance tests. The statistical analysis supports the conjecture that each participant has its own but consistent rating behavior throughout the respective sessions for standing and seated viewing. The finding that the quality rating behavior of the individual participant does not fundamentally change over time may, e.g., assist in scheduling subjective tests under pandemic conditions where experimental campaigns may need to be stalled for an unknown period of time. © 2021 IEEE. open access

Quality of Service (QoS) metrics deal with network quantities, e.g. latency and loss, whereas Quality of Experience (QoE) provides a proxy metric for end-user experience. Many papers in the literature have proposed mappings between various QoS metrics and QoE. This paper goes further in providing analysis for QoE versus bandwidth cost. We measure QoE using the widely accepted Mean Opinion Score (MOS) rating. Our results naturally show that increasing bandwidth increases MOS. However, we extend this understanding by providing analysis for internet access scenarios, using TCP, and varying the number of TCP sources multiplexed together. For these target scenarios our analysis indicates what MOS increase we get by further expenditure on bandwidth. We anticipate that this will be of considerable value to commercial organizations responsible for bandwidth purchase and allocation.

With the development of converged optical-wireless networks, the mobile users can access a very high bandwidth, any time and anywhere, for the upcoming real-time applications. In the modern digital world, quality-of-experience (QoE) has become the central area of concern to ensure end-to-end user satisfaction. In this paper, we analyze the quality-of-service (QoS) for different traffic classes for a radio-over-fiber (RoF) based attocell network. For this, we use a simple polling algorithm with dynamic weighted queuing for grant sizing and maintains a delay within the maximum bounds for high and medium priority traffic classes with high channel utilization. Further, we propose a model to map QoE with the QoS parameters, like delay, jitter, and packet loss rate, and quantify QoE by representing the degree of user satisfaction by a mean opinion score.

Quality of Service (QoS) metrics deal with network quantities, e.g. latency and loss, whereas Quality of Experience (QoE) provides a proxy metric for end-user experience. Many papers in the literature have proposed mappings between various QoS metrics and QoE. This paper goes further in providing analysis for QoE versus bandwidth cost. We measure QoE using the widely accepted Mean Opinion Score (MOS) rating. Our results naturally show that increasing bandwidth increases MOS. However, we extend this understanding by providing analysis for internet access scenarios, using TCP, and varying the number of TCP sources multiplexed together. For these target scenarios our analysis indicates what MOS increase you get by further expenditure on bandwidth. We anticipate that this will be of considerable value to commercial organizations responsible for bandwidth purchase and allocation., Comment: 16 pages

The rise in popularity of the streaming platforms for gaming videos like Twitch.tv results in more users watching and streaming gaming videos passively. These streaming videos show great dependence on Quality of Service (QoS) parameters like latency and packet loss. This motivates us to evaluate the Quality of Experience (QoE) of three popular streaming game videos for different Packet Loss Ratios (PLR). In this paper, using streaming gaming videos, we demonstrated the limitations of using Mean Opinion Score (MOS) to evaluate QoE. Using a novel method of data representation, we presented our results as distributions of user-rating at different PLR configuration and compared them with other subjective QoE evaluation methods like MOS and standard deviation of Opinion score (SOS). Our results show that evaluating QoE using MOS loses information about user diversity. Our findings also include SOS retaining user diversity but posing challenges to quantifying the QoE at varying PLR values. For streaming video games we show that QoE distributions evaluate user perception accurately. Our work in this paper also studies how different games with different content complexity, genre and pace are affected by PLR. Our results show that slow-paced games like FIFA 20 get affected more significantly by packet loss.

Designing a resource allocation technique for a cognitive radio network (CRN) adopting underlay dynamic spectrum access (DSA), requires a cross-layer approach for an acceptable quality of experience (QoE) in view of having differentiated quality-of-service (QoS) classes of traffic. This paper focuses on heterogeneous traffic management in underlay CRNs, where the transmission parameters are optimized to not only satisfy the interference constraint of the primary link but meet the delay requirement of the video traffic. The paper develops a deep reinforcement learning-based resource allocation technique which significantly lowers the queuing delay at the data link layer and maximizes the network average end-to-end mean opinion score (MOS). Simulation results show the effectiveness of the proposed cross-layer technique over a single-layer approach. The cross-layer technique effectively changes the transmission parameters in accordance with the delay requirement of the video traffic and the underlay DSA interference constraint without compromising the MOS.

The reliable delivery of packets is the major concern for the entire Internet service providers (ISPs) using Voice over internet protocols (VoIP). Therefore, quality of service (QoS) is a fundamental task for all VoIP using organizations. Unfortunately, there is no guaranteed way to ensure QoS, due to the fact that the QoS labels may be peeled off the packet while in the transition through various network elements. The performance of a network can be enhanced by Integrating QoS with internet protocol (IP) and multiprotocol label switching (MPLS) networks. In this paper we analyzed the QoS performance of VoIP in converged MPLS networks. Since IP systems are exceptionally dynamic, therefore progressive traffic monitoring and administration are needed before VoIP is even transmitted in a network infrastructure. This study provides a new configuration administration over an ISP network infrastructure comprised of converged networks carrying multiple VoIP instances. It is achieved using route reflectors and route redistribution at micro level to ensure a guaranteed threshold QoS at acceptance level in adverse circumstances.

With the advance of network and multimedia technologies, the traffic of network video services has dramatically increased. In order to support the QoS (Quality of Service) and QoE (Quality of Experience) of network video services, it is important to model network layer QoS parameters and application layer QoE appropriately. This paper presents a statistical model between network layer QoS parameters and MOS (Mean Opinion Score) values, by analyzing the statistical characteristics of the video bit rate and jitter in given MOS ranges. Compared with existing approaches, this model can provide video service providers and network operators with more appropriate suggestions for setting network layer QoS parameters under given user's QoE level, and thus has good application prospect in related field.

In the context of QoE management, network and service providers commonly rely on models that map system QoS conditions (e.g., system response time, paket loss, etc.) to estimated end user QoE values. Observable QoS conditions in the system may be assumed to follow a certain distribution, meaning that different end users will experience different conditions. On the other hand, drawing from the results of subjective user studies, we know that user diversity leads to distributions of user scores for any given test conditions (in this case referring to the QoS parameters of interest). Our previous studies have shown that to correctly derive various QoE metrics (e.g., Mean Opinion Score (MOS), quantiles, probability of users rating "good or better", etc.) in a system under given conditions, there is a need to consider rating distributions obtained from user studies, which are often times not available. In this paper we extend these findings to show how to approximate user rating distributions given a QoS-to-MOS mapping function and second order statistics. Such a user rating distribution may then be combined with a QoS distribution observed in a system to finally derive corresponding distributions of QoE scores. We provide two examples to illustrate this process: 1) analytical results using a Web QoE model relating waiting times to QoE, and 2) numerical results using measurements relating packet losses to video stall pattern, which are in turn mapped to QoE estimates. The results in this paper provide a solution to the problem of understanding the QoE distribution in a system, in cases where the necessary data is not directly available in the form of models going beyond the MOS, or where the full details of subjective experiments are not available., Comment: 4th International Workshop on Quality of Experience Management (QoE Management 2020), featured by IEEE Conference on Network Softwarization (IEEE NetSoft 2020), Ghent, Belgium

Quality of service (QoS) and quality of experience (QoE) are two major concepts for the quality evaluation of video services. QoS analyzes the technical performance of a network transmission chain (e.g., utilization or packet loss rate). On the other hand, subjective evaluation (QoE) relies on the observer's opinion, so it cannot provide output in a form of score immediately (extensive time requirements). Although several well-known methods for objective evaluation exist (trying to adopt psychological principles of the human visual system via mathematical models), each of them has its own rating scale without an existing symmetric conversion to a standardized subjective output like MOS (mean opinion score), typically represented by a five-point rating scale. This makes it difficult for network operators to recognize when they have to apply resource reservation control mechanisms. For this reason, we propose an application (classifier) that derivates the subjective end-user quality perception based on a score of objective assessment and selected parameters of each video sequence. Our model integrates the unique benefits of unsupervised learning and clustering techniques such as overfitting avoidance or small dataset requirements. In fact, most of the published papers are based on regression models or supervised clustering. In this article, we also investigate the possibility of a graphical SOM (self-organizing map) representation called a U-matrix as a feature selection method. Web of Science 12 9 art. no. 1535

Adaptive quality management schemes in Voice over IP (VoIP) Network have a constructive impact on the improvement of voice quality level received by end-users. These schemes are used for persistent monitoring of quality level leading to better management of network services. In this paper, a model is introduced to improve the voice quality level and optimize resource utilization. In the proposed model, the VoIP network makes the decision for configuration adaptively and modifies itself by means of Hidden Markov Model (HMM). Depending on the quality level and consumed bandwidth evaluated in the receiver side this adaptive self-modification takes place. During this procedure, acceptable weights for Quality and Quality of Service (QoS) parameters, namely, End-to-End Delay, Packet Loss, and Jitter are determined. These acquired weights help HMM model predict next optimum state properly. Another important objective of this work is to find a relation between Quality of Experience (in terms of Mean Opinion Score) and Quality of Service (in terms of End-to-End Delay and Jitter) parameters. This mapping provides a tangible understanding of how QoS parameters affect MOS. Simulation results demonstrate that the proposed technique lessens the impact of jitter and packet loss on voice quality level.

In recent years, the variety and volume of multimedia services have increased exponentially. Like most of other multimedia services, the conversational video service has stringent quality of service and experience requirements. In order to better support users QoE (Quality of Experience) and allocate network resources more effectively, this paper focuses on the QoS (Quality of Service)-QoE association modeling of conversational video flows. Through statistical analysis of the network video flow data, statistical distribution association between QoE and some network layer QoS parameters of the conversational video service is studied by use of the ITU-T P.1201.1 model, and relevant distribution functions are fitted. From the perspective of user’s experience, the QoS parameter recommendation indices are given for each MOS (Mean Opinion Score) value interval, and a QoE connected QoS model is established. Through the verification and comparison with an existing work, the rationality of the recommended QoS indices is demonstrated, which can help the network operators strike a balance between saving network resources and satisfying user’s quality of experience.

The adoption of smart phones, the increased access to mobile broadband networks and the availability of public cloud infrastructures are aligning to the next generation of truly ubiquitous multimedia services, known as Cloud Mobile Media (CMM) services offering mobile video. Nevertheless, due to an inherit higher and variable end to end delay mainly as a result of the virtualization process, new challenges appear. One challenge is given by live video streaming applications when trying to keep a good Quality of Experience of the delivered video, measured in terms of a subjective video quality metric, named Mean Opinion Score (MOS). Our goal is to estimate and predict this subjective metric in a holistic manner using different estimation techniques, such as Artificial Neural Networks, Factor Analysis and Multinomial Linear Regression, with Full Reference and Non Reference approaches. For this, we have analyzed and measured different variables related to Quality of Service, bit stream and basic video quality metrics, throughout the CMM infrastructure. With these variables, we apply the mentioned techniques which allows us to estimate MOS of the delivered video in a robust and reliable way, achieving an average estimation error between 0.46 and 15.94% depending on the technique used. The real MOS has been evaluated through surveys. Finally, we compare the accuracy of the estimated MOS against well known publicly available video quality algorithms, following the recommendations given by Video Quality Experts Group.

Current mobile networks are providing high speed access to Internet at a rate of Gigabits per second. In this scenario, traditional services over wired networks are an alternative, in particular those based on live video streaming. But in the transition, different issues should be considered due to the rapid changing network conditions and the limited resources of the mobile devices. These issues should be taken into account to keep a good Quality of Experience (QoE) of the video in terms of a high Mean Opinion Score (MOS), a subjective video quality. Our goal is to estimate and predict this subjective metric in a holistic manner. Thus, we have analyzed and measured different variables related to Quality of Service, bit stream and basic video quality metrics of live video streaming over LTE Advanced (4G) mobile networks, as well as server side. With these variables, we apply a statistical method, called Factor Analysis based on the correlation of the variables, which allows us to estimate MOS of the delivered video in a robust and reliable way, both in Full Reference and Non Reference approaches. The real MOS has been evaluated through surveys. Finally, we compare the accuracy of the estimated MOS for the different approaches and compare their performance with well known publicly available video quality algorithms.

In a wireless disaster response network, how does the quality of experience (QoE) of the user affect the level of situational awareness (SA)? Is maximum QoE necessary for high SA? In this empirical study, we propose a novel measurement approach to quantify SA based on the QoE of the user. The relationship between QoE and network quality of service (QoS) metrics such as delay and packet loss is well known. Therefore, quantifying the SA-QoE-QoS relationship will help the network operator to ensure a high level of SA when the network is under load, through parsimonious allocation of network resources such as spectrum and power. We first define an objective expression for SA in four contexts: surroundings awareness, target awareness, location awareness, and responsiveness (SETLR model). Using empirical data gathered from real-world experiments, we mathematically formulate SA as a function of QoE and show that this relationship is logistic in nature. An important observation is that maximum QoE is not necessary to ensure high SA; a mean opinion score of just 2-3 is necessary in our scenario. Next, we show through simulations that this new mathematical model of SA can be instantiated in a long-term evolution radio access network and efficiently used in network optimization techniques to avoid over-provisioning of resources.

Many researchers have discussed various topics in universal mobile telecommunication system (UMTS) networks: the process of switching from one cell to another for the subscriber and the impact of the quality of the connection during the transition process, quality of services (QoS), the quality of the uplink and downlink carrier line, the various types of code for the voice transmitted through the Internet, especially the research that discussed voice over internet protocol (VoIP) technology as voice travels from cell to cell in mobile networks, depending on the type of delivery. In this paper, a proposed scenario of a UMTS network was implemented to evaluate the multicellular VoIP movement; the proposed UMTS network was simulated using the OPNET 14.5 simulator. The calculation and analysis of the different parameters of the user while moving from one cell to another with different movement speeds considered, the best mean opinion score (MOS) value (3.19) registered for the scenario (soft handover) comparing with another type of handover (3.00).

MANET is one of the wireless networks in which interest is growing due to its flexibility, scalability and non-infrastructure status which is characterized by dynamically distributed clusters of nodes that have the ability to communicate locally and with the Internet. Voice over MANET-IP has become an attractive and popular application. This paper investigates the voice traffic application over the Ad Hoc on Demand Vector reactive routing protocol for different mobility models. The simulation evaluation using OPNET demonstrates the advantage of the Global System for Mobile Communication voice codec over the Internet using the UDP/IP protocol. The voice signaling and the Session Initiation IP (SIP) application layer protocols were tested and compared using different parameters. Different scenarios of mobility and node capacities were considered to test Quality of Service indicators including end-to-end delay, jitter, throughput, load, mean opinion score of voice quality, traffic sent and received, control signals, physical locations of calling nodes and the number of hops. The results showed that using the voice signaling produced better performance in respect of delays when compared to the SIP session. Voice has performed better when compared to the three mobility models with slightly less throughput and longer delays when considering the very fast mobility model. The node capacity showed serious impact on all performance metrics when MANET capacity exceeds 50 nodes in general.

Online video streaming is one of the most popular yet challenging services being used today where the Internet Service Providers (ISPs) need to guarantee certain assured service quality level for the end user. However, as the data transmission takes place over an unreliable error-prone channel; it is a challenging task for the ISPs. Normally, the user perceived quality is best expressed in terms of the Quality of Experience (QoE) factor or Mean Opinion Score (MOS) factor. On the contrary, the various factors that affect the video streaming quality; commonly referred to as the Quality of Service (QoS) factors, are relatively easier and economic to be measured by the service providers. In this paper, we consider five network layer specific QoS impairment factors (variable initial delay, packet loss, jitter, bandwidth and buffering delay) and try to assess their impact on the QoE by considering each one of them separately. We use a non-linear regression approach to compare the various QoS to QoE mapping models and find out the most suitable/optimal model for our individual cases based upon a decision variable.

Apart from video rate (or requested bitrate), Mean Opinion Score (MOS) has increasingly become a primary term representing Quality of Experience (QoE) in HTTP adaptive streaming (HAS). By monitoring this metric, QoE management can effectively maximize QoE for the users. However, due to various behaviors of particular commercial HAS players, deciding an appropriate monitoring interval has not been fully investigated yet. In this paper, an optimal interval is proposed to be equal to duration of a video chunk in order to aid service managers in early detecting QoE deterioration and limiting the probability of video rate deterioration. The optimal monitoring interval is evaluated by comparing with other values of interval in terms of ratio of video rate deterioration. Furthermore, MOS-based QoE monitoring method which takes into account the proposed interval is thus compared with video rate based monitoring method. The results show that with optimal interval, MOS monitoring guarantees a low ratio of video rate deterioration (around 10% for buffering state and 40% for steady state) and small average CPU Load (about 11.45%).

Quality-of-experience (QoE) driven resource allocation will play a huge role in the deployment of cognitive radio networks (CRNs) towards the 5G era. Cognitive approaches need to exhibit a certain degree of intelligence in order to draw optimal decisions when dealing with heterogeneous traffic. However, with current methods such as reinforcement learning (RL), efficient exploration of the state space still remains a major challenge. The epsilon (e)-greedy exploration strategy suffers from poor convergence since it does not carry out deep exploration. This consequently results in more convergence cycles or exponentially large data requirements. The docitive paradigm, which is an extension of the cognitive paradigm, performs well using RL but it becomes unfeasible when the network becomes distributed with a large number of nodes. Thus, it becomes a huge challenge to perform user experience evaluation of multimedia services on-the-fly since this requires the collection and correlation of a mixture of variables on network conditions, the service, as well as the user. As our first step towards addressing this problem, we perform resource allocation using the mean opinion score (MOS) for heterogeneous traffic. We then demonstrate that deep Q-networks (DQNs) can combine deep exploration with deep neural networks (DNNs) for exponentially faster learning and convergence. Our results indicate that docitive approaches have a 55% better convergence rate compared to individual learning at 5 SUs and 70% better at 42 SUs. Also, perfect docition performs 6.67% better than the other approaches when the number of SUs reaches 32.

In this paper a new method to improve the mathematical estimation of Quality of Experience (QoE) perceived by the end user of a Voice call over LTE (VoLTE) is presented. The new estimation leads to Mean Opinion Score (MOS) values more realistic than those available on network monitoring processes. The optimization of the QoE is calculated as a function of several QoS parameters, in particular packet loss, network jitter delay and jitter buffer dimension. The improvement has been realized by identifying the QoE and QoS parameters, named respectively Key Quality Indicators (KQI) and Key Performance Indicators (KPI), for VoLTE services, and then applying a QoE/QoS mathematical relationship in order to adjust the expected QoE level on the basis of QoS.

Voice over Internet Protocol (VoIP) is a technology that can pass voice, video, and data over an Internet Protocol (IP) network. This study aims to implement VoIP using softphone applications based on Session Initiation Protocol (SIP) at the Said Na’um Education Foundation and to recognize both the voice quality resulting from the communication made using VoIP as well as the bandwidth required for voice quality that will be used appropriately. This study used Trixbox CE as the VoIP server while the client used two softphone applications installed on the client device for testing, 3CX for computers and Zoiper for the smartphone. The bandwidth system was set to 64, 128, and 256 Kbps and the voice quality was measured in terms of two tests, Quality of Service (QoS) and Mean Opinion Score (MOS). Quality was measured twice a day, in the morning and in the evening, and the results were compared to learn about its network traffic. The results of this study show that implementation of VoIP using softphone applications based on SIP works well in terms of the results of QoS and MOS, and a minimum bandwidth for the communication on VoIP was obtained at 64 Kbps.

The use of video streaming services are increasing in the cellular networks, inferring a need to monitor video quality to meet users’ Quality of Experience (QoE). The so-called no-reference (NR) models for estimating video quality metrics mainly rely on packet-header and bitstream information. However, there are situations where the availability of such information is limited due to tighten security and encryption, which necessitates exploration of alternative parameters for conducting video QoE assessment. In this study we collect real-live in-smartphone measurements describing the radio link of the LTE connection while streaming reference videos in uplink. The radio measurements include metrics such as RSSI, RSRP, RSRQ, and CINR. We then use these radio metrics to train a Random Forrest machine learning model against calculated video quality metrics from the reference videos. The aim is to estimate the Mean Opinion Score (MOS), PSNR, Frame delay, Frame skips, and Blurriness. Our result show 94% classification accuracy, and 85% model accuracy (R2 value) when predicting the MOS using regression. Correspondingly, we achieve 89%, 84%, 85%, and 82% classification accuracy when predicting PSNR, Frame delay, Frame Skips, and Blurriness respectively. Further, we achieve 81%, 77%, 79%, and 75% model accuracy (R2 value) regarding the same parameters using regression.

International audience; Video streaming is a dominant contributor to the global Internet traffic. Consequently, monitoring video streaming Quality of Experience (QoE) is of paramount importance to network providers. Monitoring QoE of video is a challenge as most of the video traffic of today is encrypted. In this paper, we consider this challenge and present an approach based on controlled experimentation and machine learning to estimate QoE from encrypted video traces using network level measurements only. We consider a case of YouTube and play out a wide range of videos under realistic network conditions to build ML models (classification and regression) that predict the subjective MOS (Mean Opinion Score) based on the ITU P.1203 model along with the QoE metrics of startup delay, quality (spatial resolution) of playout and quality variations, and this is using only the underlying network Quality of Service (QoS) features. We comprehensively evaluate our approach with different sets of input network features and output QoE metrics. Overall, our classification models predict the QoE metrics and the ITU MOS with an accuracy of 63-90% while the regression models show low error; the ITU MOS (1-5) and the startup delay (in seconds) are predicted with a root mean square error of 0.33 and 2.66 respectively.

Multimedia communication over VANET let drivers, passengers to capture live scenes, share, and access stored content in order to be informed of a specific event or to be entertained and have an enjoyable trip. The main concern of video streaming transmission in this highly dynamic network is the improvement of the Quality of Experience (QoE). Indeed, one of the critical issues in VANET is the design of suitable routing protocol dealing with the QoE requirements. In this paper, we focus on multi-hop level routing and we propose a Delay and Quality of Link aware cross-layer Routing (DQLR) protocol enhancing the perceived video quality. In the aim to find out a route from communicating vehicles, we define a set of fuzzy rules for simultaneously minimizing the end-to-end delay and the Packet Loss Ratio and maximizing the qualities of links. To demonstrate the efficiency of DQLR for video streaming, we compare it with the AOMDV protocol in terms of QoE and QoS parameters. The simulation results show that our protocol achieves better performance compared to AOMDV in terms of PSNR (Peack Signal to Noise Ratio), MOS (Mean Opinion Score), SSIM (Structural SIMilarity), end-to-end delays and frame loss ratio.

In mobile networks, there exists a relationship between the quality of service (QoS) and the quality of experience (QoE). This is often determined by the mean opinion score (MOS). There are several approaches that are used to determine the MOS in mobile networks and these may be subjective and objective. While the subjective approaches are standardized in their own essence, they are expensive and laborious to perform. Often, objective approaches have been used to determining the MOS by using network parameters such as delay, jitter and packet loss that are normally derived from network interfaces. The main aim of this study is to reduce the cost of the capital expenditure (CAPEX) and the operational expenditure (OPEX) linked to the drive testing method of obtaining the MOS. Thus, in this paper, a cost-effective objective MOS evaluation method based on the core network parameters from the Nb interface of a 2G and 3G mobile network are used. The training and verification of the proposed model was evaluated using machine learning techniques with regression analysis. The experimental results of the proposed model were compared with the subjective MOS evaluation from the drive test. Generally, the results showed that there was a high correlation between the predicted MOS and the values collected through the drive test, which is a subjective MOS evaluation approach.

Introduction and penetration of high-speed and innovative mobile data services including social media and video services have been increasing significantly across the globe. This growth is driven by deployment and availability of capable and affordable mobile networks and smartphones. These trends have also been observed in Ethiopia as the country’s sole incumbent operator ethio telecom has expanded coverage and capacity of its mobile data network, especially for universal mobile telecommunications system (UMTS) network. The successful provision of these innovative services requires mobile operators to have continuous monitoring of the quality of their networks and customer satisfaction. To that end, this monitoring allows the operator to make timely network optimization, marketing and other relevant decisions that enhance service quality. Traditionally measurement and analysis of network quality of service (QoS) is performed using data from network management system (NMS) and drive/walk test (DT/WT). In recent times, the usage QoS data from crowdsourcing (CS) is also emerging as useful monitoring tool. However, all these methods do not fully capture users’ quality of experience (QoE), which is a function of not only network QoS but also other factors including subjective users’ technical literacy and opinion. As a result, operators have recently provided attention not only for QoS but also QoE. Yet, most quality related analyses are performed using data from only subjective survey, NMS, DT/WT, or CS. It is not straightforward to find quality investigation based on data from all the methods. More importantly, such quality investigation has not been made in the context of Ethiopia’s mobile market. In this paper, we perform QoS and QoE evaluation for UMTS mobile data service based on quality data from all NMS, WT, CS and subjective survey for a business area in Addis Ababa. The evaluation is made using downlink user throughput and mean opinion score (MOS) metrics. The obtained 50th percentile throughput results from NMS, WT and CS are good enough for web browsing and offline video streaming but not for high definition (HD) video calls and live HD video streaming. Furthermore, CS provides optimistic results compared to NMS and WT and its 90th percentile throughput is very good even for live HD video streaming. Moreover, the obtained MOS results from subjective survey almost fit to the QoS results, thus indicating that the network QoS is a significant contributor to users’ perceived QoE.

The IMS P2P network is a solution that allows the improvement of the IMS network to support p2p architectures in respect of access network technologies, in this article we propose the collaboration between IMS and the adaptation of quality with scalable video coding and peer-to-peer networks. The proposed scheme has been tested and evaluated in NS2, the proposed architecture demonstrated the adaptation of SVC video quality using a complex model of adaptation based on user performance (CPU, RAM, Bandwidth, battery life), In order to respect the heterogeneity of the equipment, so that each user receives only the flow that suits their performance. We will also make two types of subjective evaluation (Mean Opinion Score (MOS)) and objective (peak signal to noise ratio (PSNR)) and so we will evaluate the various network parameters,. Average end-to-end delay transmission of video packets and jitter on delay transmission of video packets

A Local Area Network is designed and simulated to investigate the effect of Quality of Service (QoS) on the performance of the network. The experiment examines the performance of non-QoS scenario and QoS scenario. Voice Jitter, Voice Mean Opinion Score value, Voice Packet End-to-End Delay, Voice Packet Delay Variation, Voice traffic received, and Voice traffic sent are used as performance parameters. The result demonstrates that QoS scenario outperformed non-QoS scenario. Therefore, the Quality of Service (QoS) has improved the overall performance of the network.

In Smart Cities, Vehicular Ad-Hoc Networks (VANETs) will cover an important role with a development of safety application and smart vehicle video surveillance services. With an on-board screen and smart components, multimedia communication over VANETs let drivers, passengers to capture live scenes, share, and access on-road multimedia services. In a monitored area, vehicles can transmit live video streaming of disasters or traffic accidents and provide significant visual information. In this research we propose an application offering multiple services in a smart city’s VANET. As, the main concern of the video streaming transmission in such highly dynamic environments is the enhancement of the Quality of Experience (QoE), one of the critical issues in VANET is the design of a proper routing protocol which handle this variety of services. In this paper, we focus on multi-hop level forwarding and we propose a Delay, Quality of Link and Link lifeTime aware routing protocol suitable for Video streaming (DQLTV). In the aim to find out a route from communicating vehicles, we define a mathematical model for simultaneously maximizing the quality of link and link lifetime and minimizing the distance coupled with hop count in order to minimize the delay. To demonstrate the effectiveness of DQLTV, we compare it with the AODV-ETX protocol in terms of QoE and QoS metrics. The simulation results show that our model outperforms AODV-ETX in terms of QoE metrics, the PSNR (Peack Signal to Noise Ratio), MOS (Mean Opinion Score) and SSIM (Structural SIMilarity). Our protocol also outperforms AODV-ETX in terms of QoS metrics, the end-to-end delays and frame loss.

Voice over Internet Protocol (VoIP) is a method of transmitting audio and video over a network. It is a low-cost and readily available alternative to traditional landline phones. In this paper we discuss the foundational protocols of VoIP and analyze the Quality of Service (QoS) of several applications. Our testing consisted of capturing packets during a conversation to determine packet loss, throughput, delay, and jitter. Several listeners subsequently provided Mean Opinion Scores (MOS) of several audio snippets based on phonetic clarity. We determine that Skype provides the highest quality and most consistent service.

We present in this paper a comprehensive review and comparison of recent downlink scheduling approaches for video streaming traffic over the Orthogonal Frequency Division Multiple Access (OFDMA) based Long-Term Evolution (LTE) wireless technology. Focusing on content-aware downlink scheduling approaches, we provide an extensive literature review, a taxonomy for content-aware and content-unaware downlink schedulers, and tables that summarize the key approaches and common parameters among the schedulers. In addition, we analyze and compare via simulation the performance of some of the most relevant scheduling rules. Our main goal is to compare and analyze different classes of scheduling strategies in terms of network centric performance metrics as well as user centric metrics. Quality of Service (QoS) evaluation involves the evaluation of network performance parameters, e.g., packet loss rate, average system throughput and end-to-end packet delay. On the other hand, Quality of Experience (QoE) reflects the user’s experience and satisfaction in terms of Mean Opinion Score (MOS). According to simulation results, proxy based QoE aware scheduling strategies perform best in terms of number of satisfied users and should be used in an LTE downlink to offer high quality video streaming services.