Real-Time Video Quality Control For Multimedia Network

The Quality of Experience (QoE) determines the user satisfaction degree of communication services. Quantifying the relationship between User QoE and Network Quality of Service (QoS) enables the services providers to estimate the contribution of network performance in overall user satisfaction. Based on the Multiple Linear Regression (MLR) and the exponential hypothesis, we propose in this paper a global correlation model between QoE and QoS. The main contribution of our proposal is the use of a combination of more than one QoS parameter in the model. The proposed method is validated for Video on Demand services where the QoE considers the quality of video and the QoS is impacted with two parameters (delay and loss ratio). According to the experimental results, we obtained a composite correlation model with a good correlation coefficient.

The relationship between the user perceived Quality of Experience (QoE) with Internet applications and the Quality of Service (QoS) of the underlying network and applications is complex. Unveiling statistical relations between QoE and QoS can boost the prediction and diagnosis of QoE. In this paper, we shed light on the relationship between QoE and QoS for a popular application: YouTube video streaming. We conducted a controlled study where we asked users to rate their perceived quality of YouTube videos under different network conditions. During this experiments, we also captured network QoS and application QoS. We then analyze the resulting dataset with SES, a feature selection algorithm that identifies multiple, minimal-size, statistically-equivalent signatures with maximal predictive power for a target variable (e.g., QoE). We found that we can build optimal QoE predictors using a minimal signature of only three features from application or network QoS metrics compared to four when we consider features from both layers.

Recent advances in Wireless Mesh Networks (WMNs) have overcome the drawbacks of traditional wired and ad-hoc networks and now they are seen as a means of allowing last mile communications with quality level assurance in Future Multimedia Systems. However, new routing schemes are needed to provide end-to-end Quality of Service (QoS) and Quality of Experience (QoE) support for delay/loss/jitter-sensitive multimedia applications. The well-known OLSR (Optimized Link State Routing) protocol with ETX (Expected Transmission Count) metric brings many benefits to the path selection process, but has a drawback with regard to queue availability management, which reduces the system performance. This problem is caused when OLSR-EXT control messages are exchanged and the queues of mesh routers along the end-to-end communication path are overloaded. As a result, multimedia-related packets will suffer from loss/delay/jitter and the overall system performance will decrease. This paper proposes the Optimized Link State Routing-Fuzzy ETX Queue (OLSR-FEQ) protocol to overcome the limitations of OLSR-ETX regarding queue availability, QoS and QoE assurance. OLSR-FEQ optimizes network and user-based parameters by coordinating queue availability, QoS and fuzzy issues in the routing decision process as a way of allocating the best paths for multimedia applications. Performance evaluations were carried out with the Network Simulator (NS-2.34) to show the benefits of the proposed solution when compared with existing routing schemes, namely OLSR-ETX, OLSR-FLC, OLSR-MD and HWMP (IEEE 802.11s standard), regarding QoS (unsuccessful packet delivery and throughput) and QoE (PSNR, SSIM, VQM and MOS) parameters.

A service level agreement (SLA) is part of a contract between a cloud service provider (CSP) and a cloud service user (CSU) which defines and describes different agreed properties of the service. The negotiation of the SLA between a CSU and a CSP is a pivotal process in the SLA life cycle. An SLA may include many negotiable quality of service (QoS) parameters with each QoS parameter having multiple acceptable values. Different combinations of acceptable values for all QoS parameters grow exponentially as number of QoS parameters and their selectable values increase. Manually negotiating for all of the QoS parameters considering their various options can be an erroneous and diligent task with lower utility. In this paper, a time efficient flip-flop negotiation strategy for the SLAs in cloud services is presented. The flip-flop negotiation strategy is based on a time dependent 3D utility function and on a dynamic concession strategy using the polynomial extrapolation of opponent's concession. This negotiation strategy is useful for time-critical cloud services as it operates with a motive of reaching an agreement at earlier time without compromising over the utility level of the agreement. Moreover, a multi-provider concurrent negotiation method is presented in this work using the flip-flop negotiation strategy. Multiple experiments are performed (using the implementation of flip-flop negotiation strategy) to test the presented work. The results of the experiments enforce the validity of the flip-flop negotiation strategy for such cloud services where time is an important factor during the SLA negotiation process.

The scope of this work is to present a holistic approach in quality of service (QoS) and quality of experience (QoE) characterization and prediction in modern mobile communication networks. Analytically, multi radio access technologies have been deployed in order to deliver mobile services to quality demanded consumers. Quality of Experience (QoE) parameters describe the End-to-End (E2E) quality as experienced by the mobile users. These parameters are difficult to be measured and quantified. System Quality of Service (SQoS) parameters are metrics that are closely related to the network status, and defined from the viewpoint of the service provider rather than the service user. Moreover, E2E Service Quality of Service (ESQoS) parameters describe the QoS of the services and they are obtained directly from the QoE parameters by mapping them into parameters more relevant to network operators, service providers and mobile users. A useful technique for mobile network planning and optimization is to build reliable quality estimation models for mobile voice and video telephony service.

This paper addresses the issue of supporting quality of service (QoS) parameters in distributed publish-subscribe systems. It advocates that QoS parameters should be handled using the same constructs as other information regarding events, such as their type or content. At the same time, we claim that the use of a consistent set of mechanisms should not preclude to decouple the specification of QoS properties from the specification of type, subject or content-based constraints. We also advocate that QoS parameters should not be embedded on the type or content of the events. We show that some QoS parameters can only be computed in run-time, as they depend on dynamic aspects such as the location of the participants and the system load. The paper proposes a model that supports the decoupling of QoS characterization from the event characterization while, at the same time, offers a uniform treatment of both aspects.

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.

This is a report on the Workshop on Tracking Quality of Experience in the Internet, held at Princeton, October 21--22, 2015, jointly sponsored by the National Science Foundation and the Federal Communication Commission. The term Quality of Experience (QoE) describes a user's subjective assessment of their experience when using a particular application. In the past, network engineers have typically focused on Quality of Service (QoS): performance metrics such as throughput, delay and jitter, packet loss, and the like. Yet, performance as measured by QoS parameters only matters if it affects the experience of users, as they attempt to use a particular application. Ultimately, the user's experience is determined by QoE impairments (e.g., rebuffering). Although QoE and QoS are related---for example, a video rebuffering event may be caused by high packet-loss rate---QoE metrics ultimately affect a user's experience. Identifying the causes of QoE impairments is complex, since the impairments may arise in one or another region of the network, in the home network, on the user's device, in servers that are part of the application, or in supporting services such as the DNS. Additionally, metrics for QoE continue to evolve, as do the methods for relating QoE impairments to underlying causes that could be measurable using standard network measurement techniques. Finally, as the capabilities of the underlying network infrastructure continues to evolve, researchers should also consider how to design infrastructure and tools can best support measurements that can better identify the locations and causes of QoE impairments. The workshop's aim was to understand the current state of QoE research and to contemplate a community agenda to integrate ongoing threads of QoE research into a collaboration. This summary report describes the topics discussed and summarize the key points of the discussion. Materials related to the workshop are available at http://aqualab.cs.northwestern.edu/NSFWorkshop-InternetQoE.

Multimedia applications have expanded and resulted in huge demands for high resolution videos in order to enhance the end user Quality of Experience (QoE). Therefore, QoE becomes a critical factor that is often addressed in design considerations of Consumer Electronics (CE) devices. Furthermore, error prone transmission conditions can influence the QoE, potentially degrading the end-user multimedia experience. Although video coding standards such as Versatile Video Coding (VVC) take potential mitigation measures to improve the bandwidth reductions, it fails to address other significant network Quality of Service (QoS) parameters that impact transmission based QoE such as transmission delay and Packet Loss Rate (PLR) in the encoding processes. In response, this paper first derives a relationship between QoE that addresses the network QoS parameters and VVC dependent parameters. Subsequently, the derived QoE model is used with the VVC encoding process to adaptively optimize Lagrange parameters as well as the quantization parameter. Extensive experiments report significant improvements to VVC Test Model (VTM) 7.0, achieving superior QoE gains over the state-of-the-art research and up to 3.41% over the anchor implementations.

With the rapid growth of functionally identical web services, Quality of Service (QoS) plays vital role for deciding the most suitable services for consumers. Although, QoS for web services has gained extensive attention in the literature, most of the existing efforts are unable to consider the multiperspective QoS of web services. Unlike conventional software paradigms, web services are provided, developed and used by different users such as provider, consumer and broker. As a result, QoS requirements vary from user to user. Existing researches are unable to provide standard solution, which can deal with what service providers, consumers and broker should expose in service description as QoS parameters. Therefore, in this book chapter, a multiperspective PCB-QoS classification of non-functional parameters along with new QoS parameters, i.e. access rate and Overall Aggregated Effective Quality of Service (OAEQoS) is presented. Due to the extensible nature of PCB-QoS classification, it can be extended to accommodate more number of QoS parameters. This QoS classification is based on the idea of partitioning the generic QoS parameters according to multi-user’s perspective. An Average Real Value Sum Method (ARSM) based on default priority constraints is also proposed for ranking of web services that uses the presented QoS classification. With the help of proposed approach, the user can specify the service requirements without taking the hurdle of specifying the weights for each QoS parameter during web service operations. It also prevents the services from malicious service provider to publish the incorrect and inaccurate QoS information. Experimental evaluation shows the effectiveness of proposed approach over the existing approach.

For multimedia applications, the routing algorithms should consider many Quality of Service (QoS) parameters. However, to find routes with two or more QoS parameters is an NP-hard problem. Therefore, recently many researchers are trying to use heuristic methods such as Genetic Algorithm (GA). In a previous work, a QoS multicast routing based on GA was proposed. However, in this algorithm, the multicast routing falls in the local minimum and cannot satisfy the application requirements. In this paper, we propose a new QoS multicast routing protocol, which has better genetic operations than the conventional algorithm, resulting in a better performance.

Long Term Evolution (LTE) brings to the theory of advanced leading-edge technologies which guarantees ubiquitous broadband access. As a result, the continuous increment in the number of user terminals (UT) and their expectation leads to the importance of managing and updating the networks as per the expectation of users. In this paper, the concept of self-tuning is used for adjusting the service priority factor in the scheduling algorithms to allocate the resource blocks (RBs) in order to give the appropriate Quality of Service (QoS) based on Quality of Experience (QoE). To access QoE-aware, a QoE model is created by using the Artificial Neural Network (ANN) algorithm to estimate the QoE score by using the QoS parameters. We propose the Particle Genetic Algorithm (PGA) to find the optimal parameter of service priority factors, and the proposed algorithm works efficiently by increasing the average QoE of the network along with maintaining the QoE threshold for each of the multi-service. The detailed comparison is presented between the proposed and reference algorithms to highlight the significance of the proposed algorithm and the simulation and analytical results show that the proposed algorithm outperforms the existing ones in terms of improving the allocation of resources under the environment of limited resources.

In an era of rapid urbanization, the quality of network services in urban environments is becoming increasingly important. This article explores efforts to improve network service quality in several sampang cities through an evaluation of Quality of Service (QoS) and user perceptions of Quality of Experience (QoE). The research is based on a survey conducted at various locations in sampang city to collect data on the reliability, speed, and availability of network services. The survey involved a diverse sample of users, covering a wide range of ages, professions, and levels of technology usage. In addition, we also analyzed users' perceptions of service quality based on their experiences of using the network in an urban environment. The results highlighted several key challenges in improving QoS, such as user density, signal interference, and network infrastructure limitations. However, we also found that strategies such as the use of advanced network technologies, such as 5G and beamforming, as well as traffic prioritization based on application type, can help to significantly improve QoS. In addition, we found a strong positive correlation between improved QoS and improved QoE, suggesting that improved quality of service has a direct impact on improving user experience. These findings provide valuable insights for network operators and policy makers in their efforts to improve network services in dense urban environments. The research also recommends further investment in network technologies and the development of policies that support more effective management of network traffic to ensure optimal quality of service.

Now we are in the era of ubiquitous computing. Internet of things (IoT) is getting matured in various parts of the world. In coming few years' billions and trillions of things will be connected to the internet. To deal with these huge number of devices in a network we need to consider Quality of Service (QoS)parameters so that system operations can be performed in a smoother way. Mathematical modelling of these QoS parameters gives an idea about which factors are needs to consider while designing any IoT-enabled system at the same time it will give the performance analysis of the system before implementation. In this paper comprehensive literature survey is done to discuss various issues related to QoS and gap analysis is also done for IoT Enabled systems. This paper proposes general steps to build a mathematical model for a system. It also proposes the mathematical model for QoS parameters like reliability, communication complexities, latency and aggregation of data for IoT. To support proposed mathematical model proof of concept also given.

Multicast routing is an effective way to communicate among multiple hosts in a network. For multimedia applications, the routing algorithms should consider many Quality of Service (QoS) parameters such as delay, cost and so on to find a new route. However, to find routes with two or more QoS parameters is an NP-hard problem. This paper describes a new evolutionary scheme for optimization of multicast QoS routing based on the hybrid of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), called HGAPSO. In HGAPSO, individuals in a new generation are created, not only by crossover and mutation operation as in GA, but also by PSO. The upper-half of the best-performing individuals in a population are regarded as elites. Instead of being reproduced directly to the next generation, these elites are first enhanced. The group constituted by the elites is regarded as a swarm, and each elite corresponds to a particle within it. In this regard, the elites are enhanced by PSO, an operation which mimics the maturing phenomenon in nature. This scheme can simultaneously optimize the cost of the tree, the maximum end-to-end delay, the average delay and the maximum link utilization. In this way, a set of optimal solutions, known as Pareto set, is calculated in only one run, without a priori restrictions. It has revealed an efficient method of the reconstruction of multicast tree topology and the experimental results demonstrated better performance than the conventional GA optimization method.