Packet-layer Model for End-user QoE Management

This paper presents a novel web-based crowdsourcing platform for the assessment of the subjective and objective quality of experience (QoE) of the video service in the cloud-server environment. The user has the option to enter subjective QoE data for video service by filling out a web questionnaire. The objective QoE data of the cloud-server, network condition, and the user device is automatically captured by the crowdsourcing platform. Our proposed system collects both objective and subjective QoE simultaneously in real-time. The paper presents the key technologies used in the development of the platform and describes the functional requirements and design ideas of the system in detail. The system collects real-time comprehensive data to enhance the quality of the user experience to provide a valuable reference. The system is tested in a real-time environment and the test results are given in terms of the system performance. The crowdsourcing platform has new features of real-time network monitoring, the client device, and cloud monitoring, which currently has not been provided by existing web platforms and crowdsourcing frameworks. The results show that 1MB buffer is filled 100% very soon after starting watching videos from the crowdsourcing platform.

Recently, as set-top box (STB) tends to become a potential converged system thanks to cloud technology, it is possible to run many applications requiring CPU-intensive processing remotely through legacy STB. Whereas, as STB normally consumes 80∼90% power level even when not in use, many ideas have proposed to reduce the standby power of it. But all of them don't describe a method how to control many kinds of applications affecting the energy consumption of clients in cloud without delay at any circumstance. So, In order to reduce the standby power of legacy STB and provide cloud services efficiently at the same time, a power saving scheme for cloud service on legacy STB is required. As the delay time of STB for receiving the rendered images from the cloud platform is an important factor on QoE (Quality of Experience), we present an efficient power saving scheme for control cloud services through legacy STB requiring fast response time and consuming the low power.

The DFG project “SDN-enabled Application-aware Network Control Architectures and their Performance Assessment” (DFG SDN-App) focused in phase 1 (Jan 2017 – Dec 2019) on software defined networking (SDN). Being a fundamental paradigm shift, SDN enables a remote control of networking devices made by different vendors from a logically centralized controller. In principle, this enables a more dynamic and flexible management of network resources compared to the traditional legacy networks. Phase 1 focused on multimedia applications and their users’ Quality of Experience (QoE). This documents reports the achievements of the first phase (Jan 2017 – Dec 2019), which is jointly carried out by the Technical University of Munich, Technical University of Berlin, and University of Wurzburg. The project started at the institutions in Munich and Wurzburg in January 2017 and lasted until December 2019. In Phase 1, the project targeted the development of fundamental control mechanisms for network-aware application control and application-aware network control in Software Defined Networks (SDN) so to enhance the user perceived quality (QoE). The idea is to leverage the QoE from multiple applications as control input parameter for application-and network control mechanisms. These mechanisms are implemented by an Application Control Plane (ACP) and a Network Control Plane (NCP). In order to obtain a global view of the current system state, applications and network parameters are monitored and communicated to the respective control plane interface. Network and application information and their demands are exchanged between the control planes so to derive appropriate control actions. To this end, a methodology is developed to assess the application performance and in particular the QoE. This requires an appropriate QoE modeling of the applications considered in the project as well as metrics like QoE fairness to be utilized within QoE management. In summary, the application-network interaction can improve the QoE for multi-application scenarios. This is ensured by utilizing information from the application layer, which are mapped by appropriate QoS-QoE models to QoE within a network control plane. On the other hand, network information is monitored and communicated to the application control plane. Network and application information and their demands are exchanged between the control planes so to derive appropriate control actions.

The increasing popularity of video (i.e., audio-visual) applications or services over both wired and wireless links has prompted recent growing interests in the investigations of quality of experience (QoE) in online video transmission. Conventional video quality metrics, such as peak-signal-to-noise-ratio and quality of service, only focus on the reception quality from the systematic perspective. As a result, they cannot represent the true visual experience of an individual user. Instead, the QoE introduces a user experience-driven strategy which puts special emphasis on the contextual and human factors in addition to the transmission system. This advantage has raised the popularity and widespread usage of QoE in video transmission. In this paper, we present an overview of selected issues pertaining to QoE and its recent applications in video transmission, with consideration of the compelling features of QoE (i.e., context and human factors). The selected issues include QoE modeling with influence factors in the end-to-end chain of video transmission, QoE assessment (including subjective test and objective QoE monitoring) and QoE management of video transmission over different types of networks. Through the literature review, we observe that the context and human factors in QoE-aware video transmission have attracted significant attentions since the past two to three years. A vast number of high quality works were published in this area, and will be highlighted in this survey. In addition to a thorough summary of recent progresses, we also present an outlook of future developments on QoE assessment and management in video transmission, especially focusing on the context and human factors that have not been addressed yet and the technical challenges that have not been completely solved so far. We believe that our overview and findings can provide a timely perspective on the related issues and the future research directions in QoE-oriented services over video communications.

The evaluation of the web‐browsing quality of experience (QoE) is difficult to complete through traditional methods (e.g. deducing formulas or setting thresholds) due to the diversity of websites and their contents. To evaluate web‐browsing QoE through a general way, the authors propose a web QoE evaluation architecture based on machine learning, consisting of two parts: traffic classification sub‐system and QoE prediction sub‐system. When evaluating user experience, traffic classification sub‐system first classifies the packets generated by visiting a website into a flowthrough some fields in the packet header, to model each website separately. The traffic classification accuracy of packets over six websites reaches 96.63%. Then, in the network layer, the traffic metric cumulative traffic volume is generated from the size and arrival time of packets. When a user visits a web page, their regression model predicts the above‐the‐fold time (ATF) and thus QoE. The output of the regression model is an exact ATF value that is mapped to user experience. In addition, reversing input variables further improves the model, which is evaluated on two popular websites. The QoE prediction results of the improved method for 5400 visits are obtained within 0.0975 s, reaching 0.9 .

With the data services have grown rapidly recent years, it has obtained any achievements for the research of data service QoS (Quality of Service) .But there is less research for the quality of user experience (QoE). In this paper, we emphasize on why quality of experience (QoE) in data service is so important and adopt UMTS classes which divide data services into four different types according to their feature. Then QoE indicators of different types are proposed and formulas of each indicator are given. A case study is presented to show how to calculate QoE indicator. Finally we give the conclusion and the further study.

Satisfying the needs of users of online video streaming services requires not only to manage the network Quality of Service (QoS), but also to address the user's Quality of Experience (QoE) expectations. While QoS factors reflect the status of individual networks, they do not comprehensively capture the end-to-end features affecting the quality delivered to the user. In this situation, QoE management is the better option. However, traditionally used QoE management models require human interaction and have stringent requirements in terms of time and complexity. Thus, they fail to achieve successful performance in terms of real-timeliness, accuracy, scalability and adaptability. This dissertation work investigates new methods to bring QoE management to the level required by the real-time management of video services. In this paper, we highlight our main contributions. First, with the aim to perform a combined network-service assessment, we designed an experimental methodology able to map network QoS onto service QoE. Our methodology is meant to provide service and network providers with the means to pinpoint the working boundaries of their video-sets and to predict the effect of network policies on perception. Second, we developed a generic machine learning framework that allows deriving accurate predictive No Reference (NR) assessment metrics, based on simplistic NR QoE methods, that are functionally and computationally viable for real-time QoE evaluation. The tools, methods and conclusions derived from this dissertation conform a solid contribution to QoE management of video streaming services, opening new venues for further research.

Developing a non-intrusive packet-layer model is required to passively monitor the quality of experience (QoE) during service. We propose a packet-layer model that can be used to estimate the video quality of IPTV using quality parameters derived from transmitted packet headers. The computational load of the model is lighter than that of the model that takes video signals and/or video-related bitstream information such as motion vectors as input. This model is applicable even if the transmitted bitstream information is encrypted because it uses transmitted packet headers rather than bitstream information. For developing the model, we conducted three extensive subjective quality assessments for different encoders and decoders (codecs), and video content. Then, we modeled the subjective video quality assessment characteristics based on objective features affected by coding and packet loss. Finally, we verified the model's validity by applying our model to unknown data sets different from training data sets used above.

The rapid increase of using cloud computing encourages service vendors to supply services with different features and provide them in a service pool. Service composition (SC) problem in cloud computing environment becomes a key issue because of the increase of service quantity and user requirements of the quality of service experience. To satisfy the demands on quality of service experience and realize an efficient algorithm for SC problem, a quality of experience (QoE) evaluation model based on fuzzy analytic hierarchy process (FAHP) for SC problem is put forward first. Then, an improved artificial bee colony (IABC) optimization algorithm for QoE based SC problem is proposed. The algorithm improves the updating mechanism of scout bees by introducing current global optimal solution to accelerate convergence velocity and eventually to improve the solution quality. Finally, the experimental results on QWS dataset show that IABC has a better performance on QoE based SC problem, compared with original ABC, PSO and DE.

Quality of Experience (QoE) is referred as level of user's satisfaction, enjoyment, learning and evaluation about the services or products. Recently quality of experience is used for improvement in product development life cycle after getting feedback from end users and service providers also use QoE to measure quality of services (QoS) of their services. Cloud computing provides services such as storage, web hosting, operating system environment, application development platform and CPU resources pay per use. End users are accessing cloud services via mobile apps, but enormous amount of temporary/cache data is generated by apps, so internal storage of mobile devices are filled quickly. Mobile device without any space in internal storage has huge impact on the performance when accessing the cloud services, which degrade the QoE of end users for particular cloud app and services. This paper presents the results of experiments conducted using two mobile devices HTC and Samsung to analyze the impact on end user's QoE during accessing cloud, when internal storage of HTC mobile device is filled and Samsung having 10 GB free space. Finally, on the basis of experimental results future changes in cloud apps are suggested for service provider to improve end user's QoE.

The measurements of Quality of Experience (QoE) are essential to research for better analysis and optimization of Multi-View and 3D (MV/3D) videos. However, there is a lack of systematic description of QoE mechanism in MV/3D videos yet. This paper aims to give a comprehensive account of QoE assessment and management for MV/3D video services. We first conclude distinctive QoE influencing factors (IFs) in MV/3D videos by dividing them into immersion and interaction related IFs. Then the methods of QoE assessment in MV/3D video services (including subjective assessment and objective assessment) are presented. Finally, we summarize QoE management for improving immersion and interaction. We hope our work is capable of providing a useful guideline on the related issues and the future research directions in MV/3D video services.KeywordsQuality of Experience (QoE)Multi-View Video (MVV)3D video

Significant progress has been made in the recent years in the development of technologies for encoders, decoders and networks. As a result, content, network, and Internet service providers can deliver video content over IP networks. To provide a high-quality service, the quality of experience (QoE) is becoming much more important. Since QoE is affected by such factors as the audiovisual content, encoding and decoding techniques, and network performance, service providers should monitor the QoE of a communication service in real time to confirm its status. To do this, a quality monitoring tool is necessary. The International Telecommunication Union - Telecommunication Standardization Sector Study Group 12 (ITU-T SG12) has studied the parametric non-intrusive assessment of audiovisual media streaming quality (P.NAMS). The P.NAMS - lower resolution application area was finally standardized as ITU-T Recommendation P.1201.1 in October 2012. The P.1201.1 model can be used for estimating audio, video, and audiovisual quality for mobile audiovisual media streaming using packet headers. Since the model analyzes only packet header information, the computational power of the model is very light, and the model can be applied to encrypted packets. This paper describes the P.1201.1 model and its performance.

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.

Betas: Deriving quantiles from MOS-QoS relations of IQX models for QoE management

This work shows the implementation of a hybrid STB (Set Top Box) that receives real-time digital signals for ISDB-Tb and IPTV. The implementation integrates middleware GINGA for interactive DTT applications based on low cost platforms. The features of the hardware platform for this implementation are analyzed so that they can integrate USB reception modules for digital TV, support interactive middleware versions and also be compatible with TV playback software by means of internet protocols. After the analysis and evaluation, we found that the most suitable board to achieve this implementation was Raspberry Pi 3. The development of the Android mobile graphic interface is also shown, which emulates a remote control by Bluetooth technology. Finally, the results of the implemented hybrid STB’s evaluation are presented; evaluation performed by reception tests of DTT channels with interactivity and free IPTV channels. Also, quantitative readings were made to test the performance and correct operation, as well as qualitative MOS polls to get the quality of user experience when handling the decoder.