Poor Quality Of Experience Research Articles

AI and Blockchain Empowered Metaverse for Web 3.0: Vision, Architecture, and Future Directions

As one of the most prominent parts of the Internet, World Wide Web (WWW) has achieved great success and penetrated every area of our lives. However, the current WWW still suffers from inefficiency, growing concerns about user privacy and data ownership, and poor Quality of Experience. Therefore, we propose a promising architecture for the next-generation WWW, Web 3.0, which is underpinned by Artificial Intelligence (AI) and Blockchain-empowered Metaverse (AIB-Metaverse). Fueled by native AI, Web 3.0 based on AIB-Metaverse can provide users with a personalized experience and enable smart decisionmaking with high efficiency. Backed by Blockchain, the AIBMetaverse- based Web 3.0 is decentralized, which can help users regain full control of data and protect the ownership of generated data while preserving privacy. Furthermore, this new architecture can provide a ubiquitous immersive experience to users during real-time interaction with digital avatars in the Metaverse. To verify the effectiveness of AI in Web 3.0, we propose an AI-based approach for Metaverse video delivery, which can significantly enhance the quality of immersive experience perceived by users in Web 3.0. In addition, we pinpoint the challenges faced by the proposed AIB-Metaverse-based Web 3.0 and highlight pertinent research directions in the future.

DCEC: D2D-Enabled Cost-Aware Cooperative Caching in MEC Networks

Various kinds of powerful intelligent mobile devices (MDs) need to access multimedia content anytime and anywhere, which places enormous pressure on mobile wireless networks. Fetching content from remote sources may introduce overly long accessing delays, which will result in a poor quality of experience (QoE). In this article, we considered the advantages of combining mobile/multi-access edge computing (MEC) with device-to-device (D2D) technologies. We propose a D2D-enabled cooperative edge caching (DCEC) architecture to reduce the delay of accessing content. We designed the DCEC caching management scheme through the maximization of a monotone submodular function under matroid constraints. The DCEC scheme includes a proactive cache placement algorithm and a reactive cache replacement algorithm. Thus, we obtained an optimal content caching and content update, which minimized the average delay cost of fetching content files. Finally, simulations compared the DCEC network architecture with the MEC and D2D networks and the DCEC caching management scheme with the least-frequently used and least-recently used scheme. The numerical results verified that the proposed DCEC scheme was effective at improving the cache hit ratio and the average delay cost. Therefore, the users’ QoE was improved.

Guided MAC to Enhance Quality of Experience of Video Data Transmission over Massive MIMO

Massive MIMO (M-MIMO) system comprises of multiple number of antennas to achieve energy- efficiency and large gains in spectral-efficiency in comparison to existing MIMO technology. High speed and Quality of Experience (QoE) of video data over wireless communication has always been a challenge for the researchers due to scarcity of the bandwidth, fading and interference. The channels with high noise corrupt the transmitted video and results in poor QoE of at the receiver. Therefore, to maintain the quality of transmitted video, it is highly desirable to identify noisy channels and avoid transmission over them. This paper deals with QoE of the transmitted video over Massive MIMO channels. The channels are categorized into two categories: good and bad depending upon the value of Signal to Interference and Noise Ratio (SINR). A channel above the minimum acceptable value (threshold) of SINR is categorized as good channel otherwise bad channel. A Guided MAC layer (GMAC) protocol is designed to transmit the video data over good channels only and to discard the transmission over bad channels.

Guided MAC to Enhance Quality of Experience of Video Data Transmission over Massive MIMO

Massive MIMO (M-MIMO) system contains hundreds of antennas at base station to achieve large gains in spectral-efficiency when compared with conventional MIMO technology. High speed and Quality of Experience (QoE) of video data over wireless communication has always been a challenge for the researchers due to scarcity of the bandwidth, fading and interference. Noisy channels corrupt the transmitted video and results in poor QoE of at the receiver. Therefore, to maintain the QoE of the video, it is highly desirable to identify noisy channels and avoid transmission over them. In the proposed approach, the channels are categorized into two categories: good or bad. If SINR value is greater than threshold value a channel is good otherwise bad. A Guided MAC layer (GMAC) protocol is designed to transmit the video data over good channels only and to discard the transmission over bad channels.

Quality Assessment of 3D Synthesized Images Based on Textural and Structural Distortion Estimation

Emerging 3D-related technologies such as augmented reality, virtual reality, mixed reality, and stereoscopy have gained remarkable growth due to their numerous applications in the entertainment, gaming, and electromedical industries. In particular, the 3D television (3DTV) and free-viewpoint television (FTV) enhance viewers’ television experience by providing immersion. They need an infinite number of views to provide a full parallax to the viewer, which is not practical due to various financial and technological constraints. Therefore, novel 3D views are generated from a set of available views and their depth maps using depth-image-based rendering (DIBR) techniques. The quality of a DIBR-synthesized image may be compromised for several reasons, e.g., inaccurate depth estimation. Since depth is important in this application, inaccuracies in depth maps lead to different textural and structural distortions that degrade the quality of the generated image and result in a poor quality of experience (QoE). Therefore, quality assessment DIBR-generated images are essential to guarantee an appreciative QoE. This paper aims at estimating the quality of DIBR-synthesized images and proposes a novel 3D objective image quality metric. The proposed algorithm aims to measure both textural and structural distortions in the DIBR image by exploiting the contrast sensitivity and the Hausdorff distance, respectively. The two measures are combined to estimate an overall quality score. The experimental evaluations performed on the benchmark MCL-3D dataset show that the proposed metric is reliable and accurate, and performs better than existing 2D and 3D quality assessment metrics.

IEEE 802.11n/ac Üzerinden Çoklu Video Yayını Analizi ve Değerlendirilmesi

There is an ever-growing interest for video applications. It is well-known that video applications have very stringent quality of service (QoS) requirements and require high data rates. To meet ever growing demands for mobile video streaming applications and stringent QoS requirements, it is required to optimise the current networks. IEEE 802.11 is one of the most widely deployed networks in the world. Therefore, this paper investigates the performance of multicast video streaming over IEEE 802.11n/ac in a mobile outdoor scenario. To this end, an end-to-end system level simulator is developed to evaluate the performance of the perceived video quality at the receiver in terms of peak signal to noise ratio (PSNR) and resource efficiency in terms of total transmission times for different application layer - forward error correction (AL-FEC) and medium access control (MAC) layer parameters. Results show that without AL-FEC protection the legacy IEEE 802.11n/ac provides very poor quality of experience. However, the perceived video quality can be significantly improved by adding some extra repair symbols. Since video applications have very challenging QoS requirements, changing only MAC parameters are unable to meet these QoS requirements thus additional AL-FEC protection is required. Moreover, it is observed that with the use of the frame aggregate mechanism at the MAC layer, the total transmission times can be significantly reduced and hence the resource consumptions.

Deep Learning-Based Fault Localization in Video Networks Using Only Client-Side QoE

Maintaining a satisfactory customer Quality of Experience (QoE) is of vital importance for video service providers such as Netflix or Amazon Prime Video. Network faults degrade QoE and must therefore be detected, isolated, and fixed. However, this is difficult because each part of the end-to-end path belongs to a different autonomous system (AS) that is typically owned by a different entity, such as the video streaming provider, the internet service provider (ISP), and the client's local network operator. Although the video service provider (VSP) is usually blamed by the customer when there is poor QoE, the VSP does not have access to many parts of the network to localize the issue. In this paper, we show that with the aid of AI, it is possible for the VSP to localize the network fault without having access to the faulty part and using only QoE metrics. We collected a dataset from an actual video streaming testbed, where multiple videos are streamed from a video server through a simplified ISP network to a client network. Actual faults were generated in both the ISP and the client networks. Using only the QoE metrics measured at the client side, we use the deep learning methods of multi-layer perceptron (MLP) and long-short-term memory (LSTM) to detect and localize the fault with an accuracy of 93–97%, depending on the situation. Impact Statement —Technologically, our work impacts video/game streaming service providers such as Netflix, YouTube, Amazon Prime, Google Stadia, Sony PlayStation Now, Nvidia GeForce Now, and videoconferencing providers such as Zoom and Skype. Our work enables these providers to train similar AI systems that can localize network problems using only the video quality of experience (QoE) recorded by their client software. They can then take an appropriate action, such as rerouting traffic using Open Connect Appliances (OCA) if available, using another network provider if they have contracts with more than one, or informing the owner of the network segment with the fault, so they can fix the problem and maintain their customers’ QoE at a satisfactory level. Economically, our work can contribute to the market expansion of any video streaming solution because it will lead to better QoE, which is synonymous with more customers.

Skipping-based handover algorithm for video distribution over ultra-dense VANET

Next-generation networks will pave the way for video distribution over vehicular Networks (VANETs), which will be composed of ultra-dense heterogeneous radio networks by considering existing communication infrastructures to achieve higher spectral efficiency and spectrum reuse rates. However, the increased number of cells makes mobility management schemes a challenging task for 5G VANET, since vehicles frequently switch among different networks, leading to unnecessary handovers, higher overhead, and ping-pong effect. In this sense, an inefficient handover algorithm delivers videos with poor Quality of Experience (QoE), caused by frequent and ping-pong handover that leads to high packets/video frames losses. In this article, we introduce a multi-criteria skipping-based handover algorithm for video distribution over ultra-dense 5G VANET, called Skip-HoVe. It considers a skipping mechanism coupled with mobility prediction, Quality of Service (QoS)- and QoE-aware decision, meaning the handovers are made more reliable and less frequently. Simulation results show the efficiency of Skip-HoVe to deliver videos with Mean Opinion Score (MOS) 30% better compared to state-of-the-art algorithms while maintaining a ping-pong rate around 2%.

Band Steering Technology Based on QoE-Oriented Optimization in Wireless Network

In mobile crowd-sensing (MCS) network, it is necessary to reduce the cost of sensing data transformation from mobile devices to the MCS platform in the core of the network. With the widespread deployment of access point (AP), explosive data traffic of the mobile devices is offloaded from cellular base stations to Wi-Fi networks. The congestion of the 2.4 GHz band in dual-band wireless area network makes the lower quality of experience (QoE) of users. To ease the load of 2.4 GHz band, the band-steering technology has been widely used by equipment manufacturers. However, the fixed threshold configuration strategy in band steering technology makes it easy to be exploited by setting up a great number of connection requirements in a short time, which may prevent a normal connection requirement of a user, making it difficult to satisfy the dynamic resource requirements of different services. Moreover, if the threshold is configured too high, it may result in QoE imbalance in dual bands. Besides, when the band (such as 2.4 GHz) resources cannot meet the terminal requirement, the terminal cannot autonomously switch to another band (such as 5 GHz), and this may result in a very poor QoE of users. In this paper, a secure QoE-oriented wireless network optimization framework is proposed to improve the QoE of users in dual-band network. Firstly, to ensure the overall QoE of network and balancing the QoE between 2.4 GHz and 5 GHz bands, the framework dynamically adjusts the load threshold of the frequency band based on QoE information during the association of a terminal. Secondly, to meet the basic QoE requirement of each user, the framework preferentially switches the terminals with poor QoE to the idle band after users join the network. The experimental results show that the proposed framework can improve the QoE both before and after the terminal accesses the network. Additionally, it enables the better QoE balance in dual bands.

A Services Routing Based Caching Scheme for Cloud Assisted CRNs

With the emergence of Internet of Things, the number of connected devices has been dramatically increasing, causing severe spectrum shortage problem. To fully explore the spectrum resources, big data, and cloud computing can be employed by cognitive radio networks, to make efficient use of various sensing results from different sensing sources. However, the massive growth of sensing data brings tremendous load pressure on the data center, resulting in long service response time and poor Quality of Experience. Edge computing and fog computing deal with these issues by placing computation resources at the network edge. However, compared with the data center, the capabilities at edge servers are limited. Therefore, a services routing-based caching scheme (SRCS) is proposed, which can greatly lighten the load on the data center and maintain the advantages of global intelligent computing of traditional cloud computing. Specifically, SRCS first introduces the concept of transmitting service flow. At the edge layer, data are converted to service flow by network hardware and software, thus achieving the network architecture centered on service computing. Then, SRCS proposes a service routing based on service similarity, transmits similar services through the same path, and service data are fused on the path to minimize transmission load. Moreover, SRCS caches services in content routers (CRs). When the service is requested again, CRs are used as service providers to return data, thus achieving the nearest access to the content. Both theoretical analysis and experiment results demonstrate that comparing existing schemes, SRCS improves service response time by 13.67%–51.15%, reduces transmitting data amount by 23.62%–30.3%, and makes energy consumption more balanced.

Content and buffer status aware packet scheduling and resource management framework for video streaming over LTE system

With the development of the video encoding and wireless communication technologies, DASH (Dynamic Adaptive Streaming over HTTP) services have an increasing and great share of all the mobile services. However, we find some problems which still need to be addressed for DASH service optimization: (1) the limitation of the video segment representations cannot keep pace with the change of wireless channel states; (2) the characteristics of the video transmission have been not fully utilized in current DASH services; and (3) long interruption time will yield poor QoE (Quality of Experience). To solve these problems, we build a new transmission architecture by modifying the traditional TCP transmission flow. We first add a TCP (Transmission Control Protocol) proxy responsible for discarding the expiry packets automatically before putting them into the TCP sending window and then build a “ACK (acknowledgement) reconstruction module” to reconstruct the received ACK for concealing the packets discarded by the packet scheduler. Based on the new framework, we explore the interdependence among all the packets from the encoder to indicate the importance of each packet and update the interdependence relationship of every scheduling period based on the feedback ACK information. At MAC (Media Access Control) layer, a buffer status estimation module is employed to estimate the client buffer and playback information, which can be used to calculate the packet urgency. Then, a cross-layer design, which consists of an application layer of media server and client, TCP layer, MAC layer, and physical layer, is formulated and the packet scheduling and resource allocation can be jointly optimized. At client, through the analysis of the status of client buffer and MAC queue, an adaptive segment request scheme is developed to determine how and when to send the segment request. Simulation results show that the proposed algorithms can efficiently improve the received video quality as well as the playback continuity compared with other existing algorithms.

Perceived Performance of Top Retail Webpages In the Wild

Clearly, no one likes webpages with poor quality of experience (QoE). Being perceived as slow or fast is a key element in the overall perceived QoE of web applications. While extensive effort has been put into optimizing web applications (both in industry and academia), not a lot of work exists in characterizing what aspects of webpage loading process truly influence human end-user's perception of the \emph{Speed} of a page. In this paper we present \emph{SpeedPerception}, a large-scale web performance crowdsourcing framework focused on understanding the perceived loading performance of above-the-fold (ATF) webpage content. Our end goal is to create free open-source benchmarking datasets to advance the systematic analysis of how humans perceive webpage loading process. In Phase-1 of our \emph{SpeedPerception} study using Internet Retailer Top 500 (IR 500) websites, we found that commonly used navigation metrics such as \emph{onLoad} and \emph{Time To First Byte (TTFB)} fail (less than 60\% match) to represent majority human perception when comparing the speed of two webpages. We present a simple 3-variable-based machine learning model that explains the majority end-user choices better (with $87 \pm 2\%$ accuracy). In addition, our results suggest that the time needed by end-users to evaluate relative perceived speed of webpage is far less than the time of its \emph{visualComplete} event.

Crowd-Cache: Leveraging on spatio-temporal correlation in content popularity for mobile networking in proximity

Mobile capped plans are being increasingly adopted by mobile operators due to an exponential data traffic growth. Users then often suffer high data consumption costs as well as poor quality of experience. In this paper, we introduce a novel content access scheme, Crowd-Cache, which enables mobile networking in proximity by exploiting the transient co-location of devices, the epidemic nature of content popularity, and the capabilities of smart mobile devices. Crowd-Cache provides mobile users access to popular content cheaply with low latency while improving the overall quality of experience. We model the Crowd-Cache system in a probabilistic framework using a real-life dataset of video content access. The simulation results show that, in a public transportation scenario, more than 80% of the passengers can save at least 40% on their cellular data usage during a typical average city bus commute of 10 minutes. Finally, we show the practical viability of the system by implementing and evaluating the system on Android devices.

Mobile phone applications in management of enuresis: The good, the bad, and the unreliable!

The proliferation of medical-type applications or 'apps' on smartphones is a typical example of the impact technology has had on medical practice. Maintaining a bladder diary is a recommended part of evaluating the effect of interventions for patients suffering from enuresis. Traditional pen-and-paper bladder diaries have poor completion rates, inconsistent patterns in data entry, and are deficient in validation. Electronic bladder diaries have been proposed to overcome these obstacles. With increasing numbers of smartphone apps available to the general public, it is important to distinguish well-designed apps for childhood enuresis. To identify, evaluate, and rank all available mobile-phone apps for the management of childhood enuresis. On August 21, 2014, a search was conducted on iTunes, Android Play Store, and BlackBerry World for smartphone apps using the following search terms: bladder, bedwetting, bladder diary, enuresis, incontinence, and wetting. Apps that did not have a bladder diary function and that were unrelated to the investigation, follow-up, and treatment of childhood enuresis were excluded. Apps were rated by a paediatric urology consultant, fellow, registrar, and resident medical officer using standardised criteria including: design; ease of use; languages; quality of instructions; security; accordance with ICCS definition of enuresis; and ability to store histories; record bowel habits; transfer data to other devices; and print data. Across all three search platforms, a total of 1041 apps were identified. Only 24 were included and reviewed based on exclusion criteria. Average ratings for apps ranged from 10 to 30.75 out of 50 based on standardised criteria. Smartphone apps are playing an increasingly significant role in the management of enuresis in place of pen-and-paper bladder diaries. Apps available to the general public vary in quality and it can be difficult for patients to identify one appropriate for use. We found apps with higher ratings consistently had engaging interfaces, were easy to use, and defined the primary purpose clearly. Lower ratings and performance often was caused by poor quality of experience through "freezing"/"crashing." Bladder diary apps can eliminate disadvantages of pen-and-paper diaries in the management of enuresis. Currently, apps available vary in quality. The three best-rated apps currently available are My Dryness Tracker, Bedwetting Tracker, and HapPee Time. There is room for medical associations to collaborate with developers for further app development.

Toward green media delivery: location-aware opportunities and approaches

Mobile media has undoubtedly become the predominant source of traffic in wireless networks. The result is not only congestion and poor quality of experience, but also an unprecedented energy drain at both the network and user devices. In order to sustain this continued growth, novel disruptive paradigms of media delivery are urgently needed. We envision that two key contemporary advancements can be leveraged to develop greener media delivery platforms: The proliferation of navigation hardware and software in mobile devices has created an era of location awareness, where both the current and future user locations can be predicted; and the rise of context-aware network architectures and self-organizing functionalities is enabling context signaling and in-network adaptation. With these developments in mind, this article investigates the opportunities of exploiting location awareness to enable green end-to-end media delivery. In particular, we discuss and propose approaches for location-based adaptive video quality planning, in-network caching, content prefetching, and long-term radio resource management. To provide insights on the energy savings, we then present a cross-layer framework that jointly optimizes resource allocation and multi-user video quality using location predictions. Finally, we highlight some of the future research directions for location-aware media delivery in the conclusion.