scholarly journals Deep Neural Network–based Enhancement for Image and Video Streaming Systems: A Survey and Future Directions

2022 ◽  
Vol 54 (8) ◽  
pp. 1-30
Author(s):  
Royson Lee ◽  
Stylianos I. Venieris ◽  
Nicholas D. Lane
Keyword(s):  
Deep Learning ◽  
Quality Of Experience ◽  
Super Resolution ◽  
Fast Response ◽  
Content Delivery ◽  
Delivery Systems ◽  
Live Streaming ◽  
Design Decisions ◽  
Future Directions

Internet-enabled smartphones and ultra-wide displays are transforming a variety of visual apps spanning from on-demand movies and 360°  videos to video-conferencing and live streaming. However, robustly delivering visual content under fluctuating networking conditions on devices of diverse capabilities remains an open problem. In recent years, advances in the field of deep learning on tasks such as super-resolution and image enhancement have led to unprecedented performance in generating high-quality images from low-quality ones, a process we refer to as neural enhancement. In this article, we survey state-of-the-art content delivery systems that employ neural enhancement as a key component in achieving both fast response time and high visual quality. We first present the components and architecture of existing content delivery systems, highlighting their challenges and motivating the use of neural enhancement models as a countermeasure. We then cover the deployment challenges of these models and analyze existing systems and their design decisions in efficiently overcoming these technical challenges. Additionally, we underline the key trends and common approaches across systems that target diverse use-cases. Finally, we present promising future directions based on the latest insights from deep learning research to further boost the quality of experience of content delivery systems.

Quality of Experience in Mobile Peer-to-Peer Streaming Systems

2012 ◽  
pp. 196-241
Author(s):  
Florence Agboma
Keyword(s):  
Quality Of Experience ◽  
Business Models ◽  
Prediction Models ◽  
Content Delivery ◽  
Peer To Peer ◽  
Network Resource ◽  
Relative Contribution ◽  
Qos Parameters ◽  
Streaming Systems

This chapter considers the various parameters that affect the user’s Quality-of-Experience (QoE) in mobile peer-to-peer streaming systems, which are a form of content delivery network. Network and content providers do not necessarily focus on users’ QoE when designing the content delivery strategies and business models. The outcome of this is quite often the over-provisioning of network resources and also a lack of knowledge in respect to the user’s satisfaction. The focus is the methodology for quantifying the user’s perception of service quality for mobile video services and user contexts. The statistical technique of discriminant analysis is employed in defining prediction models to map Quality-of-Service (QoS) parameters onto estimates of the user’s QoE ratings. The chapter considers the relative contribution of the QoS parameters to predicting user responses. The chapter also demonstrates the value of the prediction models in developing QoE management strategies in order to optimize network resource utilization. To investigate the versatility of the framework, a feasibility study was applied to a P2P TV system. P2P systems continue to develop and as such, not a lot is known about their QoE characteristics, which situation this chapter seeks to remedy.

scholarly journals Mechanisms for QoE optimisation of Video Traffic: A review paper

2020 ◽  
Author(s):  
qahhar muhammad qadir ◽  
Alexander A. Kist ◽  
ZHONGWEI ZHANG
Keyword(s):  
Quality Of Experience ◽  
Review Paper ◽  
End Users ◽  
The Internet ◽  
Video Traffic ◽  
Future Directions ◽  
The Past ◽  
New Methods ◽  
Increasing Demand

Transmission of video traffic over the Internet has grown exponentially in the past few years<br>with no sign of waning. This increasing demand for video services has changed user expectation of quality. Various mechanisms have been proposed to optimise the Quality of Experience (QoE) of end users’ video. Studying these approaches are necessary for new methods to be proposed or combination of existing ones to be tailored. We discuss challenges facing the optimisation of QoE for video traffic in this paper. It surveys and classifies these mechanisms based on their functions. The limitation of each of them is identified and future directions are highlighted.

scholarly journals Mechanisms for QoE optimisation of Video Traffic: A review paper

2015 ◽  
Vol 1 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Qahhar Muhammad Qadir
Keyword(s):  
Quality Of Experience ◽  
Review Paper ◽  
The Internet ◽  
Video Traffic ◽  
Future Directions ◽  
The Past ◽  
New Methods ◽  
Video Services ◽  
Increasing Demand

Transmission of video traffic over the Internet has grown exponentially in the past few years with no sign of waning. This increasing demand for video services has changed user expectation of quality. Various mechanisms have been proposed to optimise Quality of Experience (QoE) of end user's video. Studying these approaches are necessary for new methods to be proposed or combination of existing ones to be tailored. We discuss challenges facing the optimisation of QoE for video traffic in this paper. It surveys and classifies these mechanisms based on their functions. The limitation of each of them is identified and future directions are highlighted.

scholarly journals Network Performance Criteria for Telecommunication Traffic Types driven by Quality of Experience

2019 ◽  
Vol 15 (3) ◽  
pp. 233-244 ◽  
Author(s):  
Ines Ramadža ◽  
Vesna Pekić ◽  
Julije Ožegović
Keyword(s):  
Network Performance ◽  
Quality Of Experience ◽  
Performance Criteria ◽  
Live Streaming ◽  
Traffic Routing ◽  
Different Types ◽  
Telecom Operator ◽  
The Impact ◽  
The Given

A common reason for changing the chosen service provider is the users' perception of service. Quality of Experience (QoE) describes the end user's perception of service while using it. A frequent cause of QoE degradation is inadequate traffic routing, where, other than throughput, selected routes do not satisfy minimum network requirements for the given service or services. In order to enable QoE-driven routing, per traffic type defined routing criteria are required. Our goal was to obtain those criteria for relevant services of a telecom operator. For the purpose of identifying services of interest, we first provide short results of user traffic analysis within the telecom operator network. Next, our work presents testbed measurements which explore the impact of packet loss and delay on user QoE for video, voice, and management traffic. For video services, we investigated separately multicast delivery, unicast HTTP Live Streaming (HLS), and unicast Real Time Streaming Protocol (RTSP) traffic. Applying a threshold to QoE values, from the measured dependencies we extracted minimum network performance criteria for the investigated different types of traffic. Finally, we provide a comparison with results available in the literature on the topic.

Context-Aware Content Delivery

2015 ◽  
pp. 474-499
Author(s):  
Hassnaa Moustafa ◽  
V. Srinivasa Somayazulu ◽  
Yiting Liao
Keyword(s):  
Quality Of Experience ◽  
Service Providers ◽  
Content Delivery ◽  
Context Aware ◽  
Video Delivery ◽  
Network Resources ◽  
Personalized Services ◽  
Over The Top ◽  
Required Quality

The huge changes in multimedia and video consumption styles are leading to different challenges for the current Internet architecture in order to support the required quality of experience. A comprehensive solution to these would help the service providers and over-the-top players (OTT) to differentiate their services and the network operators to handle ever growing demands on network resources in an era of slower growth in revenues. This chapter discusses the requirements for and approaches to enhanced content delivery architectures, video delivery standards and current and future content transport mechanisms. The chapter also discusses the Quality of Experience (QoE) metrics and management for video content and introduces context-awareness in the video delivery chain. It also provides several examples for context-aware content delivery and personalized services.

scholarly journals A comprehensive review of deep learning-based single image super-resolution

2021 ◽  
Vol 7 ◽  
pp. e621
Author(s):  
Syed Muhammad Arsalan Bashir ◽  
Yi Wang ◽  
Mahrukh Khan ◽  
Yilong Niu
Keyword(s):  
Deep Learning ◽  
Super Resolution ◽  
Single Image ◽  
Open Problems ◽  
Future Directions ◽  
Attention Network ◽  
Domain Specific ◽  
Feedback Network ◽  
Image Super Resolution ◽  
Single Image Super Resolution

Image super-resolution (SR) is one of the vital image processing methods that improve the resolution of an image in the field of computer vision. In the last two decades, significant progress has been made in the field of super-resolution, especially by utilizing deep learning methods. This survey is an effort to provide a detailed survey of recent progress in single-image super-resolution in the perspective of deep learning while also informing about the initial classical methods used for image super-resolution. The survey classifies the image SR methods into four categories, i.e., classical methods, supervised learning-based methods, unsupervised learning-based methods, and domain-specific SR methods. We also introduce the problem of SR to provide intuition about image quality metrics, available reference datasets, and SR challenges. Deep learning-based approaches of SR are evaluated using a reference dataset. Some of the reviewed state-of-the-art image SR methods include the enhanced deep SR network (EDSR), cycle-in-cycle GAN (CinCGAN), multiscale residual network (MSRN), meta residual dense network (Meta-RDN), recurrent back-projection network (RBPN), second-order attention network (SAN), SR feedback network (SRFBN) and the wavelet-based residual attention network (WRAN). Finally, this survey is concluded with future directions and trends in SR and open problems in SR to be addressed by the researchers.

scholarly journals Mechanisms for QoE optimisation of Video Traffic: A review paper

2020 ◽  
Author(s):  
qahhar muhammad qadir ◽  
Alexander A. Kist ◽  
ZHONGWEI ZHANG
Keyword(s):  
Quality Of Experience ◽  
Review Paper ◽  
End Users ◽  
The Internet ◽  
Video Traffic ◽  
Future Directions ◽  
The Past ◽  
New Methods ◽  
Increasing Demand

Transmission of video traffic over the Internet has grown exponentially in the past few years<br>with no sign of waning. This increasing demand for video services has changed user expectation of quality. Various mechanisms have been proposed to optimise the Quality of Experience (QoE) of end users’ video. Studying these approaches are necessary for new methods to be proposed or combination of existing ones to be tailored. We discuss challenges facing the optimisation of QoE for video traffic in this paper. It surveys and classifies these mechanisms based on their functions. The limitation of each of them is identified and future directions are highlighted.

scholarly journals Quality Enhanced Multimedia Content Delivery for Mobile Cloud with Deep Reinforcement Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Muhammad Saleem ◽  
Yasir Saleem ◽  
H. M. Shahzad Asif ◽  
M. Saleem Mian
Keyword(s):  
Quality Of Experience ◽  
Performance Metrics ◽  
Optimal Solution ◽  
Content Delivery ◽  
Multimedia Streaming ◽  
Multimedia Content ◽  
Quality Level ◽  
Limited Bandwidth ◽  
Multimedia Content Delivery

The importance of multimedia streaming using mobile devices has increased considerably. The dynamic adaptive streaming over HTTP is an efficient scheme for bitrate adaptation in which video is segmented and stored in different quality levels. The multimedia streaming with limited bandwidth and varying network environment for mobile users affects the user quality of experience. We have proposed an adaptive rate control using enhanced Double Deep Q-Learning approach to improve multimedia content delivery by switching quality level according to the network, device, and environment conditions. The proposed algorithm is thoroughly evaluated against state-of-the-art heuristic and learning-based algorithms. The performance metrics such as PSNR, SSIM, quality of experience, rebuffering frequency, and quality variations are evaluated. The results are obtained using real network traces which shows that the proposed algorithm outperforms the other schemes in all considered quality metrics. The proposed algorithm provides faster convergence to the optimal solution as compared to other algorithms considered in our work.

scholarly journals Mitigation of the Effects of Network Outage on Video QoE Using a Sender Buffer

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1209
Author(s):  
Tahir Nawaz Minhas ◽  
Markus Fiedler
Keyword(s):  
Quality Of Experience ◽  
Video Quality ◽  
Multimedia Applications ◽  
Mobile Internet ◽  
Live Streaming ◽  
End User ◽  
Analytical Relations ◽  
User Ratings ◽  
The Impact

With the growth of multimedia applications and the mobile Internet, quality sense and quality expectation of the end-user are rising rapidly. A small notable distortion in the multimedia applications may degrade the degree of delight of the user, who is very considerate of the video Quality of Experience (QoE). During live streaming, a network outage may result in video freezes and video jumps. To dampen the impact of a network outage on the video QoE, we propose the use of a well-sized sender buffer. We present the concept, derive key analytical relations, and perform a set of subjective tests. Based on those, we report a significant enhancement of user ratings due to the proposed sender buffer in the presence of network outages.

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