Video Streaming Applications Research Articles

Quality of Experience Oriented Adaptive Video Streaming for Edge Assisted Cellular Networks

HTTP adaptive streaming accounts for a large part of mobile Internet traffic. With the developing cellular communication technologies, the standard dynamic adaptive streaming over HTTP technique allows mobile terminals to adaptively configure the transmission rate of video streaming applications and achieve high quality of experiences (QoE) with many adaptive bitrate algorithms. However, existing schemes either neglect to consider radio access network (RAN) side conditions or only consider simple RAN information optimization schemes. In this letter, we propose a QoE-oriented adaptive video streaming scheme based on a dueling deep Q-learning network. The proposed scheme improves the QoE by jointly considering the physical layer transmission bandwidth and the higher layer buffer status. Through the numerical and prototyping results, we show that our proposed scheme outperforms the existing schemes, with the average QoE improvements of 12.6% to 28.8%.

Reinforcement Learning Based Multipath QUIC Scheduler for Multimedia Streaming.

With the recent advances in computing devices such as smartphones and laptops, most devices are equipped with multiple network interfaces such as cellular, Wi-Fi, and Ethernet. Multipath TCP (MPTCP) has been the de facto standard for utilizing multipaths, and Multipath QUIC (MPQUIC), which is an extension of the Quick UDP Internet Connections (QUIC) protocol, has become a promising replacement due to its various advantages. The multipath scheduler, which determines the path to which each packet should be transmitted, is a key function that affects the multipath transport performance. For example, the default minRTT scheduler typically achieves good throughput, while the redundant scheduler gains low latency. While the legacy schedulers may generally give a desirable performance in some environments, however, each application renders different requirements. For example, Web applications target low latency, while video streaming applications require low jitter and high video quality. In this paper, we propose a novel MPQUIC scheduler based on deep reinforcement learning using the Deep Q-Network (DQN) that enhances the quality of multimedia streaming. Our proposal first takes into account both delay and throughput as a reward for reinforcement learning to achieve a low video chunk download time. Second, we propose a chunk manager that informs the scheduler of the video chunk information, and we also tune the learning parameters to explore new random actions adequately. Finally, we implement our new scheduler on the Linux kernel and give results using the Mininet experiments. The evaluation results show that our proposal outperforms legacy schedulers by at least 20%.

Analyzing the Influence of Smart-device Visual Features, Viewing Distance, and Content Factors on Video Streaming QoE

Quality of experience (QoE) over wireless networks has attracted attention from industry and academia due to an increase in video streaming applications. Several researchers have attempted to understand the factors affecting QoE and design appropriate quality control strategies. Normally, video streaming is initiated by a user who accesses video contents over wireless networks using a smart device held at various viewing distances. Each aforementioned factor has the potential to affect QoE of the viewed session. However, several studies explore the behavior of wireless networks on video streaming QoE. To understand the effects of other factors on QoE, this paper investigates the influence of the device's visual features, viewing distance, and content factors on video streaming. The study adopted an emulation technique to conduct multi-factor experiments designed using the Taguchi method. The 5-ways ANOVA analysis revealed that the effects of smart-device visual features, viewing distance, and content types are significant on video streaming QoE at p<0.05. Moreover, smart devices with a pixel density index of more than 200 ppi produce high QoE, with the viewing distance limited to 45 cm. Lastly, the video bitrate greater than 1024 kbps produced a good QoE regardless of the frame rates.

The Sustainable Effect of Artificial Intelligence and Parental Control on Children’s Behavior While Using Smart Devices’ Apps: The Case of Saudi Arabia

The study was necessitated by the unprecedented consumption of smart devices by children in Saudi Arabia, which has been a concern to parents and other stakeholders. It investigated the way that game apps, social media apps, and video-streaming apps impact child development. It also examined the roles played by artificial intelligence control and parental control in enhancing the sustainability of children’s behavior amidst smart technologies. The theories underpinning this research were the theory of reasoned action and the theory of planned behavior. The population of the study was 1,616,755 households in the eastern, western, and central regions. The researcher used an online survey to capture the sentiments of a sample of 415 parents who had given their children at least one smart device. The primary questionnaire focused on game apps, social media apps, video-streaming apps, artificial intelligence control, parental control, and the sustainability of child behavior. On the other hand, a separate questionnaire designed specifically to capture demographic information was also drafted. The structural equation model (SEM) was preferred, as it depicted the moderating roles of artificial intelligence control and parental control by using SPSS AMOS software. Findings established that games, social media, and video-streaming applications negatively affected the sustainability of child behavior. The findings presented in this paper show that the moderating effect of artificial intelligence control was more statistically significant than parental controls in influencing the sustainability of child behavior. Moreover, the results show that the greatest effect on children’s behavior were social media, video-streaming, and games apps. respectively. Nevertheless, both approaches resulted in positive child behavior. Hence, the study concluded that using artificial intelligence control is more effective than relying on parental controls to enhance the behavioral sustainability of children with smart device applications in Saudi Arabia.

VDANE: Using virtualization for improving video quality with Server and Network Assisted DASH

SummaryNetwork Function Virtualization (NFV) offers flexibility in traffic engineering and network resource management, by taking advantage of Software Defined Networking (SDN). By using these network technologies, it is possible to enhance the performance of video streaming applications by placing network functions in suitable locations and rerouting flows. Our study addresses the “virtual cache placement” problem in dynamic networks, where traffic patterns and attachment points of the clients are changing rapidly. The cache placement is done by determining how many virtual caches are necessary to be able to provide acceptable service to the clients, as well as where to place those caches to meet demand. To this end, we provide a heuristic solution by taking advantage of NFV‐SDN and having the assistance of Server and Network Assisted DASH (SAND). Experimental results show that the proposed algorithms can improve the video client rebuffering by 150%–270% and also can provide an 8%–12% increase in average bitrate received by the client, compared to a number of benchmark algorithms. The obtained results indicate that the co‐operation between the client and the operator of an SDN‐enabled network, by exchanging client and network information, allows network resources to be efficiently used, and as a consequence, the Quality of Experience (QoE) on the client's side is improved.

Investigation of Video Streaming over MANETS Routing Protocols

For industrial and academic researchers from all around the world, the Mobile Ad-hoc Network (MANET) is a specific focus. A MANET was created as a result of the fast growth of wireless gadgets. Mobile Ad hoc network is a self-organizing network with no framework. MANET’s device has the ability to travel in any direction in order to transmit and receive data with other devices or network nodes. An administrator node which is capable of controlling other nodes is absent in MANET. All nodes in MANET act as its own router and host and arrange its own network. The most important element of MANET is Routing protocol. For routing in MANET, various routing protocol is responsible. Providing a video streaming service using mobile ad-hoc networks is a hard task. The network topology is immensely unstable and its instability causes data to be lost. The performance of MANET routing protocols for video applications has been examined in this paper. There are many routing protocols, however the Optimized Link State Routing (OLSR) protocol and Ad-hoc On-Demand Distance Vector (AODV) routing protocols are explored here. In this work, a comparison of these two effective routing protocols for supporting video streaming applications is offered.

Online video streaming application

Online video streaming application have made reasonable impact of video streaming technology and also the video streaming service offered have made an impact in day today life. The streaming service offered through website has re-shaped the use of online media. Particularly this technology has favored online users. The proposed streaming application implemented using React.Js as Frontend technology and MongoDB for record large number of data efficiently and Nodejs as a Backend technology. Each user can register and login with their respective credentials for the access. The video can be download locally as well as user can create a playlist and create favorites with the real time streaming transmission with multiple filters. MERN Stack is a Java script Stack that is used for easier and faster deployment of full-stack web applications. MERN Stack comprises of 4 technologies namely: MongoDB, Express and Node.js. It is designed to make the development of web applications. This application can help to add content and decrease in dwell time to search engines that the content is valuable, boosting the ranking for target keywords.

Intelligent traffic management system using Ant Colony Optimization and Internet of Things

SummaryMiniaturization of Internet of Things (IoT) enabled devices has led to advancement in Intelligent Transportation System. Some of the challenges for efficient traffic management are connection of vehicles through an IP based infrastructure for transportation, reliable, and flexible traffic control management, maintaining quality of services for video streaming applications in Internet of Vehicles and many more. Today there is a need of an efficient transportation system with improved efficiency of traffic safety and lower traveling costs. An intelligent traffic control system using Ant Colony Optimization (ACO) algorithm has been proposed and analyzed in this paper. Proposed algorithm has been compared with existing state of art algorithms in terms of average waiting time and average traveling time.

Toward Generalized Psychovisual Preprocessing For Video Encoding

Deep perceptual preprocessing has recently emerged as a new way to enable further bitrate savings across several generations of video encoders without breaking standards or requiring any changes in client devices. In this article, we lay the foundation for a generalized psychovisual preprocessing framework for video encoding and describe one of its promising instantiations that is practically deployable for video-on-demand, live, gaming, and user-generated content (UGC). Results using state-of-the-art advanced video coding (AVC), high efficiency video coding (HEVC), and versatile video coding (VVC) encoders show that average bitrate [Bjontegaard delta-rate (BD-rate)] gains of 11%–17% are obtained over three state-of-the-art reference-based quality metrics [Netflix video multi-method assessment fusion (VMAF), structural similarity index (SSIM), and Apple advanced video quality tool (AVQT)], as well as the recently proposed nonreference International Telecommunication Union-Telecommunication?(ITU-T) P.1204 metric. The proposed framework on CPU is shown to be twice faster than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times 264$ </tex-math></inline-formula> medium-preset encoding. On GPU hardware, our approach achieves 714 frames/sec for 1080p video (below 2 ms/frame), thereby enabling its use in very-low-latency live video or game streaming applications.

End-to-end Characterization of Game Streaming Applications on Mobile Platforms

With the advent of 5G, supporting high-quality game streaming applications on edge devices has become a reality. This is evidenced by a recent surge in cloud gaming applications on mobile devices. In contrast to video streaming applications, interactive games require much more compute power for supporting improved rendering (such as 4K streaming) with the stipulated frames-per second (FPS) constraints. This in turn consumes more battery power in a power-constrained mobile device. Thus, the state-of-the-art gaming applications suffer from lower video quality (QoS) and/or energy efficiency. While there has been a plethora of recent works on optimizing game streaming applications, to our knowledge, there is no study that systematically investigates the &lt;QoS, Energy&gt; design pairs on the end-to-end game streaming pipeline across the cloud, network, and edge devices to understand the individual contributions of the different stages of the pipeline for improving the overall QoS and energy efficiency. In this context, this paper presents a comprehensive performance and power analysis of the entire game streaming pipeline consisting of the server/cloud side, network, and edge. Through extensive measurements with a high-end workstation mimicking the cloud end, an open-source platform (Moonlight-GameStreaming) emulating the edge device/mobile platform, and two network settings (WiFi and 5G) we conduct a detailed measurement-based study with seven representative games with different characteristics. We characterize the performance in terms of frame latency, QoS, bitrate, and energy consumption for different stages of the gaming pipeline. Our study shows that the rendering stage and the encoding stage at the cloud end are the bottlenecks to support 4K streaming. While 5G is certainly more suitable for supporting enhanced video quality with 4K streaming, it is more expensive in terms of power consumption compared to WiFi. Further, fluctuations in 5G network quality can lead to huge frame drops thus affecting QoS, which needs to be addressed by a coordinated design between the edge device and the server. Finally, the network interface and the decoder units in a mobile platform need more energy-efficient design to support high quality games at a lower cost. These observations should help in designing more cost-effective future cloud gaming platforms.

A first look at HTTP/3 adoption and performance

The third version of the Hypertext Transfer Protocol (HTTP) is in the final standardization phase by the IETF. In addition to better security and greater flexibility, it promises performance benefits. HTTP/3 uses a more efficient header compression scheme and replaces TCP with QUIC, a transport protocol over UDP that was originally proposed by Google and is also currently being standardized. Although initial implementations of HTTP/3 already exist and some websites have announced their support, few studies have been conducted to assess its benefits.We measure the adoption and performance of HTTP/3 and show how it has been adopted by some of the leading Internet companies such as Google, Facebook, and Cloudflare in 2020. We conduct a large-scale measurement campaign on thousands of websites using HTTP/3 to understand the extent to which it outperforms HTTP/2 in web browsing applications. We find that websites using HTTP/3 often host most web page objects on third-party servers that only support HTTP/2 or even HTTP/1.1. Websites that load objects from a limited number of third-party domains are the ones that see larger performance gains. However, our experiments show that HTTP/3 offers significant benefits only in high-latency or mobile networks. Finally, we run an experimental campaign to study the impact of HTTP/3 on video streaming applications. In this direction, our results show that HTTP/3 currently does not provide benefits.

QFlow: A Learning Approach to High QoE Video Streaming at the Wireless Edge

The predominant use of wireless access networks is for media streaming applications. However, current access networks treat all packets identically, and lack the agility to determine which clients are most in need of service at a given time. Software reconfigurability of networking devices has seen wide adoption, and this in turn implies that agile control policies can be now instantiated on access networks. Exploiting such reconfigurability requires the design of a system that can enable a configuration, measure the impact on the application performance (Quality of Experience), and adaptively select a new configuration. Effectively, this feedback loop is a Markov Decision Process whose parameters are unknown. The goal of this work is to develop QFlow, a platform that instantiates this feedback loop, and instantiate a variety of control policies over it. We use the popular application of video streaming over YouTube as our use case. Our context is priority queueing, with the action space being that of determining which clients should be assigned to each queue at each decision period. We first develop policies based on model-based and model-free reinforcement learning. We then design an auction-based system under which clients place bids for priority service, as well as a more structured index-based policy. Through experiments, we show how these learning-based policies on QFlow are able to select the right clients for prioritization in a high-load scenario to outperform the best known solutions with over 25&#x0025; improvement in QoE, and a perfect QoE score of 5 over 85&#x0025; of the time.

Consumer behaviour analysis in video streaming purchases in Indonesia by implementing 7P's marketing strategy using quality function deployment

Video is a technology for capturing, recording, processing, transmitting and rearranging moving images. Videos can be packed on VCDs, DVDs, and Blue-ray discs. In the millennial era, there are many websites and video streaming applications. With smartphones, video streaming applications can be enjoyed anytime and anywhere with a variety of video genres. In addition, video streaming also sell videos legally such as http://www.iflix.com, http://www.hooq.tv, http://www.netflix.com. Video streaming can increase competition to attract conventional video products. In this case, the company needs the right strategy to increase the purchase volume by understanding consumer needs. The objective of the study is to see and analyse consumer behaviour regarding video streaming purchases to improve video streaming recognition in Indonesia. The decision to purchase by consumers is affected by 7P (product, marketing strategies price, place, promotion, people, physical evidence, and process). A 7P marketing strategies is one of ways that can be used to analyse behaviour consumptive in the buying video streaming. In this study, to get the information about the consumption of video done through the questionnaire, data obtained from respondents will be processed using the quality function deployment (QFD) method.

Dynamic Vehicular Clustering Enhancing Video on Demand Services Over Vehicular Ad-hoc Networks

Nowadays, video streaming applications are becoming one of the tendencies driving vehicular network users. In this work, considering the unpredictable vehicle density, the unexpected acceleration or deceleration of the different vehicles included in the vehicular traffic load, and the limited radio range of the employed communication scheme, we introduce the “Dynamic Vehicular Clustering” (DVC) algorithm as a new scheme for video streaming systems over vehicular ad-hoc networks (VANET). The proposed algorithm takes advantage of the small cells concept and the introduction of wireless backhauls, inspired by the different features and the performance of the Long Term Evolution (LTE)-Advanced network. Vehicles are clustered together to form dynamically ad-hoc sub-networks included in the vehicular network. The goal of our clustering algorithm is to take into account several characteristics, such as the vehicle's position and acceleration to reduce latency and packet loss. Therefore, each cluster is counted as a small cell containing vehicular nodes and an access point that is elected regarding some particular specifications. Based on the exceptional features of the LTE-Advanced network (small cells and wireless backhauls) the DVC algorithm is a promising scheme for video streaming services over VANET systems. Experiments were carried out with a virtual topology of the VANET network created with four clusters to implement the DVC algorithm. The results were compared with other algorithms such as Virtual Trust-ability Data transmission (VTD), Named Data Networking (NDN), and Socially Aware Security Message Forwarding (SASMF). Our algorithm can effectively improve the transmission rate of data packets at the expense of a slight increase in end-to-end delay and control overhead.

A Novel Analytical Framework Is Developed for Wireless Heterogeneous Networks for Video Streaming Applications

Due to bandwidth and time limits, delivering high‐quality video streaming facility offers real‐time wireless networks, but assuring quality of experience (QoE) is rather difficult. A network host can deliver its data stream via numerous different network pathways. At the same time, using multipath data transmission, where video streaming may be supplied through Internet protocols (IPs) as well as broadband with bidirectional communication between video sources and consumers, is one way to address this difficult problem. In this work, a novel framework is developed for wireless heterogeneous networks for video streaming applications using concurrent multipath transfer (CMT). The performance of frame‐level delay can be enhanced for better video quality using this proposed method. The network congestion on end‐to‐end path utilised in CMT is not dependent of one another, and we operate under prior assumption that receiver’s announced window does not limit the sender. The analytical results show that the proposed framework outperforms existing methods in terms of performance with minimum retransmission delay.

Determinants of Millennial behaviour towards current and future use of video streaming services

PurposeThis study aims to investigate the evolution of factors that influence the current and future use of video streaming applications by Millennial consumers.Design/methodology/approachCombining technology acceptance, perceived values and user identity theory this study used factor analysis and multiple regression to examine data from a survey of 292 university undergraduates.FindingsMillennial’s current and future use of video streaming services remains driven more by social and emotional values and their effect on identity salience with their choice of content. Ease of use, convenience and monetary value remains less of an influence currently but may become more important in the future with the continued maturity of the industry.Practical implicationsThe results of this study suggest that video streaming service providers should be developing business models that recognize the increasing importance of emotional appeal and self-identity of their service offerings as the industry matures and competition increases.Originality/valueThe research is novel in addressing future video streaming service provision by examining changes in young consumer behaviour over time within a similar sample population and considering the growth and technological advancement of video streaming services. The results are significant in addressing the gap that exists in understanding whether perceived values for technology adoption of the same product or service by millennials change over time and the implications that have for product and service providers.

Supervised-learning-Based QoE Prediction of Video Streaming in Future Networks: A Tutorial with Comparative Study

The Quality of Experience (QoE) based service management remains key for successful provisioning of multimedia services in next-generation networks such as 5G/6G, which requires proper tools for quality monitoring, prediction and resource management where machine learning (ML) can play a crucial role. In this paper, we provide a tutorial on the development and deployment of the QoE measurement and prediction solutions for video streaming services based on supervised learning ML models. Firstly, we provide a detailed pipeline for developing and deploying supervised learning-based video streaming QoE prediction models which covers several stages including data collection, feature engineering, model optimization and training, testing and prediction and evaluation. Secondly, we discuss the deployment of the ML model for the QoE prediction/measurement in the next generation networks (5G/6G) using network enabling technologies such as Software-Defined Networking (SDN), Network Function Virtualization (NFV) and Mobile Edge Computing (MEC) by proposing reference architecture. Thirdly, we present a comparative study of the state-of-the-art supervised learning ML models for QoE prediction of video streaming applications based on multiple performance metrics.

FastTrack: Minimizing Stalls for CDN-Based Over-the-Top Video Streaming Systems

Traffic for internet video streaming has been rapidly increasing and is further expected to increase with the higher definition videos and IoT applications, such as 360 degree videos and augmented virtual reality applications. While efficient management of heterogeneous cloud resources to optimize the quality of experience is important, existing work in this problem space often left out important factors. In this paper, we present a model for describing a today’s representative system architecture for video streaming applications, typically composed of a centralized origin server and several CDN sites. Our model comprehensively considers the following factors: limited caching spaces at the CDN sites, allocation of CDN for a video request, choice of different ports from the CDN, and the central storage and bandwidth allocation. With the model, we focus on minimizing a performance metric, stall duration tail probability (SDTP), and present a novel, yet efficient, algorithm to solve the formulated optimization problem. The theoretical bounds with respect to the SDTP metric are also analyzed and presented. Our extensive simulation results demonstrate that the proposed algorithms can significantly improve the SDTP metric, compared to the baseline strategies. Small-scale video streaming system implementation in a real cloud environment further validates our results.

On Improving the Robustness of MEC with Big Data Analysis for Mobile Video Communication

Mobile video communication and Internet of Things are playing a more and more important role in our daily life. Mobile Edge Computing (MEC), as the essential network architecture for the Internet, can significantly improve the quality of video streaming applications. The mobile devices transferring video flow are often exposed to hostile environment, where they would be damaged by different attackers. Accordingly, Mobile Edge Computing Network is often vulnerable under disruptions, against either natural disasters or human intentional attacks. Therefore, research on secure hub location in MEC, which could obviously enhance the robustness of the network, is highly invaluable. At present, most of the attacks encountered by edge nodes in MEC in the IoT are random attacks or random failures. According to network science, scale-free networks are more robust than the other types of network under the random failures. In this paper, an optimization algorithm is proposed to reorganize the structure of the network according to the amount of information transmitted between edge nodes. BA networks are more robust under random attacks, while WS networks behave better under human intentional attacks. Therefore, we change the structure of the network accordingly, when the attack type is different. Besides, in the MEC networks for mobile video communication, the capacity of each device and the size of the video data influence the structure significantly. The algorithm sufficiently takes the capability of edge nodes and the amount of the information between them into consideration. In robustness test, we set the number of network nodes to be 200 and 500 and increase the attack scale from 0% to 100% to observe the behaviours of the size of the giant component and the robustness calculated for each attack method. Evaluation results show that the proposed algorithm can significantly improve the robustness of the MEC networks and has good potential to be applied in real-world MEC systems.

A novel no-sensors 3D model reconstruction from monocular video frames for a dynamic environment.

Occlusion awareness is one of the most challenging problems in several fields such as multimedia, remote sensing, computer vision, and computer graphics. Realistic interaction applications are suffering from dealing with occlusion and collision problems in a dynamic environment. Creating dense 3D reconstruction methods is the best solution to solve this issue. However, these methods have poor performance in practical applications due to the absence of accurate depth, camera pose, and object motion.This paper proposes a new framework that builds a full 3D model reconstruction that overcomes the occlusion problem in a complex dynamic scene without using sensors’ data. Popular devices such as a monocular camera are used to generate a suitable model for video streaming applications. The main objective is to create a smooth and accurate 3D point-cloud for a dynamic environment using cumulative information of a sequence of RGB video frames. The framework is composed of two main phases. The first uses an unsupervised learning technique to predict scene depth, camera pose, and objects’ motion from RGB monocular videos. The second generates a frame-wise point cloud fusion to reconstruct a 3D model based on a video frame sequence. Several evaluation metrics are measured: Localization error, RMSE, and fitness between ground truth (KITTI’s sparse LiDAR points) and predicted point-cloud. Moreover, we compared the framework with different widely used state-of-the-art evaluation methods such as MRE and Chamfer Distance. Experimental results showed that the proposed framework surpassed the other methods and proved to be a powerful candidate in 3D model reconstruction.