Satisfactory Quality Of Experience Research Articles

A GNN-based proactive caching strategy in NDN networks

As people spend more time watching and sharing videos online, it is critical to provide users with a satisfactory quality of experience (QoE). Leveraging the in-network caching and named-based routing features in Named Data Networks (NDNs), our paper aims to improve user experience through caching. We propose a graph neural network-gain maximization (GNN-GM) cache placement algorithm. First, we use a GNN model to predict users’ ratings of unviewed videos. Second, we consider the total predicted rating of a video as the gain of the cached video. Third, we propose a cache placement algorithm to maximize the caching gain and actively cache videos. Cache replacement is implemented based on the cache gain ranking of videos, with higher cache gain videos replacing lower cache gain videos. We compare GNN-GM with two state-of-the-art caching strategies, namely the NMF-based caching strategy and GNN-CPP. GNN-GM is also compared with two traditional caching strategies, LCE and LRU, LCE and FIFO. We evaluate the five caching strategies using real-world datasets in a tree network topology, a real-world network topology GEANT, and various random topologies. The experimental results show that our caching policy significantly improves cache hit ratio, latency and server load. Notably, GNN-GM achieves a 25% higher cache hit rate, 5% lower latency and 7% lower server load than GNN-CPP in GEANT.

Content Caching Based on Popularity and Priority of Content Using seq2seq LSTM in ICN

Efficient content caching is essential to address the explosive growth of multimedia contents and most works on content cache placement have been proposed mainly based on the popularity of content. Since the priority of content is also an important attribute of content, we consider both popularity and priority of content together for content caching in information-centric networking (ICN). We define weighted delivery cost of content as content delivery cost multiplied by a weighted sum of popularity and priority of the content. Then, we formulate optimized content cache placement problem to minimize weighted content delivery cost for all content requests in a hierarchical network architecture with multi-access edge computing (MEC) and software-defined networking (SDN) controller. Average quality of experience (QoE), i.e., average content delivery cost, for contents with each priority is imposed as constraint. The number of content requests is predicted based on seq2seq long short-term memory (LSTM) model in MECs and this is delivered to SDN controller. Then, SDN controller obtains predicted popularity of contents and decides content placement in MECs and core routers by solving the content cache placement optimization problem based on binary particle swarm optimization (BPSO). Performance of the proposed content caching scheme is compared with conventional popularity-based, popularity prediction-based, and popularity prediction-based optimization schemes, from the aspect of QoE satisfaction ratio, average cost, weighted average cost, total cost, and weighted total cost. Numerical results show the effectiveness of the proposed scheme at caching content with high priority efficiently, at the expense of caching content with low priority.

Blockchain-Enabled Conditional Decentralized Vehicular Crowdsensing System

The rapid growth of connected and autonomous vehicles (CAVs) shows an urgent demand for driving and transportation-related data, which gives rise to vehicular crowdsensing systems (VCSs). Nevertheless, the existing centralized VCS framework mainly faces the system reliability problem while the decentralized one cannot satisfy the management flexibility. In addition, when the privacy preservation scheme that prevents information leakage encounters the user selection scheme that desires detailed information of participants, how to balance this seemingly irreconcilable contradiction is inevitable for VCS. To remedy that, we take the first research attempt and explore the balance point between the system management, privacy preservation, and quality of experience (QoE) of participants. By fully exploiting the characters of participating entities, a blockchain-enabled conditional decentralized VCS is proposed in this paper. Firstly, we propose a privacy-preserving scheme where the zk-SNARK proof combines with the mixed-task smart contract to guarantee the interaction process will not reveal any private information of participants. Secondly, we propose an efficient reputation management mechanism that renders certain the participants can get a satisfactory QoE even under the condition that the private information of users is secured. And also, the malicious operations in the system will be effectively supervised. Theoretical analysis and extensive simulations demonstrate the security and efficiency properties of privacy preservation and indicate the effectiveness of reputation management.

QoE-Aware Traffic Aggregation Using Preference Logic for Edge Intelligence

Traffic flows with different requirements of quality of service (QoS requirements) are aggregated into different QoS classes to provide differentiated services (Diffserv) and better quality of experience (QoE) for users. The existing aggregation approaches/QoS mapping methods are based on quantitative QoS requirements and static QoS classes. However, they are typically qualitative and time-varying at the edge of the beyond fifth generation (B5G) networks. Therefore, the artificial intelligence technology of preference logic is applied in this paper to achieve an intelligent method for edge computing, called the preference logic based aggregation model (PLM), which effectively groups flows with qualitative requirements into dynamic classes. First, PLM uses preferences to describe QoS requirements of flows, and thus can deal with both quantitative and qualitative cases. Next, the potential conflicts in these preferences are eliminated. According to the preferences, traffic flows are finally mapped into dynamic QoS classes by logic reasoning. The experimental results show that PLM presents better performance in terms of QoE satisfaction compared with the existing aggregation methods. Utilizing preference logic to group flows, PLM implements a novel way of edge intelligence to deal with dynamic classes and improves the Diffserv for massive B5G traffic with quantitative and qualitative requirements.

On QoE-Oriented Cloud Service Orchestration for Application Providers

New virtualization technologies allow Infrastructure Providers (InPs) to lease their resources to Application Service Providers (ASPs) for highly scalable delivery of cloud services to end-users. However, existing literature lacks knowledge on Quality of Experience (QoE)-oriented cloud service orchestration algorithms that can guide ASPs on how to plan their budget to enhance satisfactory QoE delivery to end-users. In contrast to the InP's cloud service orchestration, the ASP's orchestration should not rely on expensive infrastructure control mechanisms such as Software-Defined Networking (SDN), or require aprioriknowledge on the number of services to be instantiated and their anticipated placement location within InP's infrastructure. In this paper, we address this issue of delivering satisfactory user QoE by synergistically optimizing both ASP's management and data planes. The optimization within the ASP management planefirst maximizes Service Level Objective (SLO) coverage of users when application services are being deployed, and are not yet operational. The optimization of the ASP data plane then enhances satisfactory user QoE delivery when applications services are operational with real user access. Our evaluation of QoE-oriented algorithms using realistic numerical simulations, real-world cloud testbed experiments with actual users and ASP case studies show notably improved performance over existing cloud service orchestration solutions.

A New Efficient Architecture for Adaptive Bit-Rate Video Streaming

The demand for multimedia content over the Internet protocol network is growing exponentially with Internet users’ growth. Despite high reliability and well-defined infrastructure for Internet protocol communication, Quality of Experience (QoE) is the primary focus of multimedia users while getting multimedia contents with flawless or smooth video streaming in less time with high availability. Failure to provide satisfactory QoE results in the churning of the viewers. QoE depends on various factors, such as those related to the network infrastructure that significantly affects perceived quality. Furthermore, the video delivery’s impact also plays an essential role in the overall QoE that can be made efficient by delivering content through specialized content delivery architectures called Content Delivery Networks (CDNs). This article proposes a design that enables effective and efficient streaming, distribution, and caching multimedia content. Moreover, experiments are carried out for the factors impacting QoE, and their behavior is evaluated. The statistical data is taken from real architecture and analysis. Likewise, we have compared the response time and throughput with the varying segment size in adaptive bitrate video streaming. Moreover, resource usage is also analyzed by incorporating the effect of CPU consumption and energy consumption over segment size, which will be counted as effective efforts for sustainable development of multimedia systems. The proposed architecture is validated and indulged as a core component for video streaming based on the use case of a Mobile IPTV solution for 4G/LTE Users.

Forecasting Video QoE With Deep Learning From Multivariate Time-Series

The end users’ satisfactory Quality of Experience (QoE) is a fundamental criterion for networked video service providers such as video-on-demand providers (Netflix, YouTube, etc.), cloud gaming providers (Google Stadia, PlayStation Now, etc.) and videoconferencing providers (Zoom, Microsoft Teams, etc.). To know the QoE, providers today typically predict it from the Quality of Service (QoS) parameters or the client-side's actual QoE metrics measured at the current time-step. But the former does not precisely reflect the users' experience, and the latter has a delay between QoE measurements at the client-side and the user's current experience. Mitigating this delay can provide a noticeable improvement in the delivery system's performance. For example, accurate forecasting of QoE for the near future allows the service management system to take a proactive approach and fix delivery issues before they become a noticeable problem at the end user, or at least reduce overall QoE degradation. QoE forecasting can also be used in rate adaptation in DASH or resource allocation in wireless networks. In this paper, we propose a method to prognosticate QoE metrics. Using data collected from an industry video streaming testbed for three different classes, we define a multivariate time series forecasting problem. We then model a hybrid state-of-the-art deep learning method, BiLSTM-CNN, to forecast the QoE metrics in advance. Evaluation of our proposed method compared to four other well-known ML models of Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), Long short-term memory (LSTM), and Bidirectional LSTM (BiLSTM) demonstrates the superior performance of our proposed method.

AI-enabled mobile multimedia service instance placement scheme in mobile edge computing

Leveraging cloud infrastructure to the mobile edge computing helps the mobile users to get real time multimedia services in Fifth Generation (5G) network system. To ensure higher Quality-of-Experience (QoE), faster migration of mobile multimedia service instances is required to cope up with user mobility. By deploying the mobile multimedia service instances proactively in multiple edge nodes (ENs) helps the users to get higher QoE. However, excessive deployment of service replicas might increase the cost of the overall network. To establish trade-off between these two conflicting objectives, we have formulated the problem as a Multi-objective Integer Linear Programming (MILP) by integrating the users’ path prediction model. This problem is proven to be an NP-hard one for large networks, thus we develop an artificial intelligence (AI) based meta-heuristic Binary Particle Swarm Optimization (BPSO) algorithm to achieve near-optimal solution within polynomial time. The performance analysis results show the significant performance improvement in terms of QoE and user satisfaction as compared to other state-of-the-art works.

Mechanism to validate and compare inter-device/destination media synchronization solutions adopted in multi-screen scenarios

Simultaneous consumption of related content in one (or multiple) devices (i.e., multi-screen scenarios) is commonplace. To provide a satisfactory Quality of Experience (QoE) in this type of scenarios it is necessary to adopt synchronization mechanisms to keep the involved content playout processes in a synchronized state. Most of those mechanisms are software-based and usually perform some estimations, at runtime, of the playout latency. Therefore, the accuracy of the measured synchronization level can be difficult to evaluate or assess. In this work, a computer vision-based mechanism to evaluate the achieved accuracy in multi-screen scenarios is proposed. Thus, the accuracy level which is obtained through the synchronization mechanisms implemented in those systems or applications can be assessed at the end point of the content presentation. Additionally, in order to show the utility of the proposed mechanism, some evaluations have been conducted for a common use case, a videowall, in which accurate synchronization is a requirement.

TCLiVi: Transmission Control in Live Video Streaming Based on Deep Reinforcement Learning

Currently, video content accounts for the majority of network traffic. With increased live streaming, rigorous requirements have been introduced for better Quality of Experience (QoE). It is challenging to meet satisfactory QoE in live streaming, where the aim is to achieve a balance between 1) enhancing the video quality and stability and 2) reducing the rebuffering time and end-to-end delay, under different scenarios with various network conditions and user preferences, where the fluctuation in the network throughput degrades the QoE severely. In this paper, we propose an approach to improve the QoE for live video streaming based on Deep Reinforcement Learning (DRL). The new approach jointly adjusts the streaming parameters, including the video bitrate and target buffer size. With the basic DRL framework, TCLiVi can automatically generate the inference model based on the playback information, to achieve the joint optimization of the video quality, stability, rebuffering time and latency parameters. We evaluate our framework on real-world data in different live streaming broadcast scenarios, such as a talent show and a sports competition under different network conditions. We compare TCLiVi with other algorithms, such as the Double DQN, MPC and Buffer-based algorithms. The simulation results show that TCLiVi significantly improves the video quality and decreases the rebuffering time, consequently increasing the QoE score by 40.84% in average. We also show that TCLiVi is self-adaptive in different scenarios.

On the Suitability of HbbTV for Unified Smart Home Experience

Hybrid broadcast broadband TV (HbbTV) is a standardization initiative aimed at harmonising the broadcast and broadband delivery of entertainment services to consumers through connected TVs, set-top boxes, and multiscreen devices. A smart home (SH) is a suitable home setup where appliances and devices can be automatically controlled remotely from any Internet-connected device. For the seamless integration of a variety of devices within the SH, HbbTV solutions are gaining a pivotal role, acting as a central hub for audio and video broadcasting services, home automation and other devices, including smartphones, tablets and Internet of Things (IoT)-enabled devices. In this context, a standard, compliant and platform independent, solution is preferred as it can enable dynamic environment where heterogeneous devices contribute to the overall user experience. This paper proposes an architecture for the seamless integration of a SH environment with a HbbTV-enabled television set and consumer’s handled devices, such as smartphones and tablets, using the HbbTV 2.0.1 Companions Screen and Multimedia Synchronization framework. A complete architectural design and its hardware implementation have been developed and deployed as a proof of concept. Subjective tests based on mean opinion score (MOS) have been conducted to assess the benefits of the proposed system in terms of Quality of Experience (QoE). Results are encouraging and show that the accurate design of hybrid services through HbbTV could lead to satisfactory QoE perceived by users in the fruition of unified TV and SH services. Based on the obtained results, a preferred common configuration of notifications in terms of position, duration, and sound was indicated.

Client-Side QoE Management for SVC Video Streaming: An FSM Supported Design Approach

HTTP adaptive streaming (HAS) which provides the flexibility of video bit-rate adjustments, is becoming the de facto framework for live and on-demand video streaming services. The flexibility of HAS is further empowered by the scalable video coding (SVC) technique which allows low overhead quality upgradations of transmitted video segments. In this paper, we present a client-side finite-state machine (FSM) supported quality of experience (QoE) centric video bit-rate adaptation and management mechanism. The proposed strategy simultaneously manages three important QoE verticals: 1) providing stutter-free video viewing experience; 2) minimizing flickers in video outputs by controlling and smoothing the rates of encoding quality switches over time; and 3) maximizing aggregate video quality over a video playout session. Based on careful consideration of these QoE verticals, the management policy dynamically decides whether to download a new segment or upgrade the quality of an already downloaded segment in the playout buffer. We have implemented and evaluated the performance of our proposed framework in a multi-client SVC video steaming test-bed. Conducted experiments using real-world network traces reveal that the proposed strategy is able to outperform other state-of-the-art adaptive streaming techniques and deliver satisfactory QoE even in the face of highly varying channel conditions.

QoE-aware Q-learning based approach to dynamic TDD uplink-downlink reconfiguration in indoor small cell networks

The continuing rise of the amount of mobile traffic is daunting, but the deploying of indoor small cells provides exciting opportunities to boost network capacity, extend cell coverage, and eventually thrive on an increased level of customers’ quality of experience (QoE). Unfortunately, in current wireless systems, traffic exhibits great variations in uplink and downlink directions, which introduces challenges of efficient resource allocation. Through using a dynamic time-division duplexing (TDD) method, network operators can flexibly adapt to such variations. However, cross-link interference appears in a dynamic TDD network and seriously suppresses uplink transmission. In this work, we proposed a decentralized QoE-aware reinforcement learning based approach to dynamic TDD reconfiguration. The objective is to maximize the utility function of the users’ QoE in an indoor small cell network. This was done by empowering each base station to select the best configuration to avoid the occurrence of cross-link interference while maintaining as many users that can enjoy their service at a satisfactory QoE as possible. At each episode, after collecting local reports of the QoE state and traffic load of the users, every base station dynamically chooses the best configuration according to the learning model. The learning process repeats itself until convergence. We implemented a simulator to evaluate the performances of the proposed algorithms. The results show that the proposed strategy achieves the best utility of QoE in comparison with other approaches, especially in the direction of the uplink transmission. The study demonstrates the great potential of harnessing reinforcement learning algorithms to attain higher QoE in small cell networks.

Using 360 VR video to improve the learning experience in veterinary medicine university degree

We present the results of the Quality of Experience (QoE) evaluation of 360 degree immersive video in university education. Fourth-year Veterinary Medicine students virtually attended some practical lessons which had been recorded in immersive 360 video format, covering topics of Surgical Pathology and Surgery related to horses. One hundred students participated in the experience. They evaluated it through an extensive questionnaire covering several QoE factors, including presence, audiovisual quality, satisfaction or cybersickness: 79% evaluated the experience as excellent or good, and they acknowledged an improvement of the learning process by the implementation of VR as didactic tool, and 91% reported that they would recommend it to other students. Female students consistently gave slightly better average scores than their male counterparts, although mostly within confidence intervals. Strongest inter-gender differences appeared in active social presence dimensions, according to the Temple Presence Inventory. The study also evaluates the suitability of synthetic measurement protocols, such as the Distributed Reality Experience Questionnaire (DREQ) and Net Promoter Score (NPS). We show that NPS is a valid tool for QoE analysis, but that its clustering boundary values must be adapted to the specificities of the experiment population.

QoE-Driven D2D Media Services Distribution Scheme in Cellular Networks

Device-to-device (D2D) communication has been widely studied to improve network performance and considered as a potential technological component for the next generation communication. Considering the diverse users’ demand, Quality of Experience (QoE) is recognized as a new degree of user’s satisfaction for media service transmissions in the wireless communication. Furthermore, we aim at promoting user’s Mean of Score (MOS) value to quantify and analyze user’s QoE in the dynamic cellular networks. In this paper, we explore the heterogeneous media service distribution in D2D communications underlaying cellular networks to improve the total users’ QoE. We propose a novel media service scheme based on different QoE models that jointly solve the massive media content dissemination issue for cellular networks. Moreover, we also investigate the so-called Media Service Adaptive Update Scheme (MSAUS) framework to maximize users’ QoE satisfaction and we derive the popularity and priority function of different media service QoE expression. Then, we further design Media Service Resource Allocation (MSRA) algorithm to schedule limited cellular networks resource, which is based on the popularity function to optimize the total users’ QoE satisfaction and avoid D2D interference. In addition, numerical simulation results indicate that the proposed scheme is more effective in cellular network content delivery, which makes it suitable for various media service propagation.

Enhancing QoE for video streaming in MANETs via multi-constraint routing

In this paper, we propose a Multi-Constraint Quality-of-Experience (QoE) centric Routing (MCQR) technique for efficient real-time video streaming over Mobile Ad-hoc Networks (MANETs). The dynamic estimation of link quality metric during MCQR route discovery process is the key of enhancing QoE at end user. MCQR limits end-to-end delay and jitter of received data packets by selecting long lifetime routes with maximum permissible delay. This minimizes the distortion caused fluctuations in perceived video. To prove the effectiveness of MCQR for real-time video streaming, we have designed a hybrid MANET consisting of simulated nodes and physical machines. In our hybrid network, the source and destination nodes are video traffic generators and receivers running on physical devices. Emulation results are obtained using real-time video streaming over high fidelity virtual wireless network, and conforming that the MCQR protocol provides satisfactory QoE to end users as compared to existing similar techniques. The packet delivery ratio of MCQR is higher than 90 % and its mean opinion score is higher than three (i.e., satisfactory) even when mobility and shadowing effects are kept high in target network scenarios. These improvements are achieved along with still maintaining similar or better routing overhead and processing delays.

Big data in mobile social networks: a QoE-oriented framework

Due to the rapid development of mobile social networks, mobile big data play an important role in providing mobile social users with various mobile services. However, as mobile big data have inherent properties, current MSNs face a challenge to provide mobile social user with a satisfactory quality of experience. Therefore, in this article, we propose a novel framework to deliver mobile big data over content- centric mobile social networks. At first, the characteristics and challenges of mobile big data are studied. Then the content-centric network architecture to deliver mobile big data in MSNs is presented, where each datum consists of interest packets and data packets, respectively. Next, how to select the agent node to forward interest packets and the relay node to transmit data packets are given by defining priorities of interest packets and data packets. Finally, simulation results show the performance of our framework with varied parameters.

Experiences and Results of Applying Tools for Assessing the Quality of a mHealth App Named Heartkeeper.

Currently, many incomplete mobile apps can be found in the commercial stores, apps with bugs or low quality that needs to be seriously improved. The aim of this paper is to use two different tools for assessing the quality of a mHealth app for the self-management of heart diseases by the own patients named Heartkeeper. The first tool measures the compliance with the Android guidelines given by Google and the second measures the users' Quality of Experience (QoE). The results obtained indicated that Heartkeeper follows in many cases the Android guidelines, especially in the structure, and offers a satisfactory QoE for its users, with special mention to aspects such as the learning curve, the availability and the appearance. As a result, Heartkeeper has proved to be a satisfactory app from the point of view of Google and the users. The conclusions obtained are that the type of tools that measure the quality of an app can be very useful for developers in order to find aspects that need improvements before releasing their apps. By doing this, the number of low-quality applications released will decrease dramatically, so these techniques are strongly recommended for all the app developers.

A Medical Application Integrating Remote 3D Visualization Tools to Access Picture Archiving and Communication System on Mobile Devices

With computing capability and display size growing, the mobile device has been used as a tool to help clinicians view patient information and medical images anywhere and anytime. However, for direct interactive 3D visualization, which plays an important role in radiological diagnosis, the mobile device cannot provide a satisfactory quality of experience for radiologists. This paper developed a medical system that can get medical images from the picture archiving and communication system on the mobile device over the wireless network. In the proposed application, the mobile device got patient information and medical images through a proxy server connecting to the PACS server. Meanwhile, the proxy server integrated a range of 3D visualization techniques, including maximum intensity projection, multi-planar reconstruction and direct volume rendering, to providing shape, brightness, depth and location information generated from the original sectional images for radiologists. Furthermore, an algorithm that changes remote render parameters automatically to adapt to the network status was employed to improve the quality of experience. Finally, performance issues regarding the remote 3D visualization of the medical images over the wireless network of the proposed application were also discussed. The results demonstrated that this proposed medical application could provide a smooth interactive experience in the WLAN and 3G networks.

Propagation-based social-aware multimedia content distribution

Online social networks have reshaped how multimedia contents are generated, distributed, and consumed on today's Internet. Given the massive number of user-generated contents shared in online social networks, users are moving to directly access these contents in their preferred social network services. It is intriguing to study the service provision of social contents for global users with satisfactory quality of experience. In this article, we conduct large-scale measurement of a real-world online social network system to study the social content propagation. We have observed important propagation patterns, including social locality, geographical locality, and temporal locality. Motivated by the measurement insights, we propose a propagation-based social-aware delivery framework using a hybrid edge-cloud and peer-assisted architecture. We also design replication strategies for the architecture based on three propagation predictors designed by jointly considering user, content, and context information. In particular, we design a propagation region predictor and a global audience predictor to guide how the edge-cloud servers backup the contents, and a local audience predictor to guide how peers cache the contents for their friends. Our trace-driven experiments further demonstrate the effectiveness and superiority of our design.