Video Streaming Technology Research Articles

Penerimaan Aplikasi Loklok Menggunakan Metode Technology Acceptance Model (TAM) oleh Masyarakat Jakarta

Nowadays, the development of communication and information technology has brought major chages in various aspect of life, one of them is in entertainment aspects. For example video streaming technology. Video streaming technology has been part of modern society. Loklok is of video streaming technology application which is popular in Indonesia. The purpose of this research is to analyze the acceptance video streaming technology on Loklok application and find out whether it can be generally acceptance by the people of Jakarta. This research was done for loklok application users. Loklok is an application or platform that provides video streaming services with many varieties of genres such Korean dramas, western films and animations. The conceptual model of technology acceptance assessment is based on the Technology Acceptance Model (TAM), which is a research model that analyzes the factors that influence the acceptance of computer technology. The statistical analysis used is the partial least squares structural equation model. This research uses quantitative methods with data collection techniques in the form of filling out online questionnaires through Google Forms with the number of respondents determined by the Slovin formula and sampling techniques using simple random sampling. This study uses PLS-SEM data analys with SmartPLS tools with a sample size of 100. The results of this study indicate that the variables Perceived Usefulness -> Attitude of use, Perceived ease of use -> Attitude of use, Attitude of use - > Behavioral Intention Of Use and Behavioral Intention Of Use -> Actually System Use are positive which means that the LokLok application is well received by the Jakarta Community.

Semantic guidance incremental network for efficiency video super-resolution

AbstractIn video streaming, bandwidth constraints significantly affect client-side video quality. Addressing this, deep neural networks offer a promising avenue for implementing video super-resolution (VSR) at the user end, leveraging advancements in modern hardware, including mobile devices. The principal challenge in VSR is the computational intensity involved in processing temporal/spatial video data. Conventional methods, uniformly processing entire scenes, often result in inefficient resource allocation. This is evident in the over-processing of simpler regions and insufficient attention to complex regions, leading to edge artifacts in merged regions. Our innovative approach employs semantic segmentation and spatial frequency-based categorization to divide each video frame into regions of varying complexity: simple, medium, and complex. These are then processed through an efficient incremental model, optimizing computational resources. A key innovation is the sparse temporal/spatial feature transformation layer, which mitigates edge artifacts and ensures seamless integration of regional features, enhancing the naturalness of the super-resolution outcome. Experimental results demonstrate that our method significantly boosts VSR efficiency while maintaining effectiveness. This marks a notable advancement in streaming video technology, optimizing video quality with reduced computational demands. This approach, featuring semantic segmentation, spatial frequency analysis, and an incremental network structure, represents a substantial improvement over traditional VSR methodologies, addressing the core challenges of efficiency and quality in high-resolution video streaming.

PAM: Predictive analytics and modules‐based computation offloading framework using greedy heuristics and 5G NR‐V2X

AbstractRecent advancements in distributed computing systems have shown promising prospects in enabling the effective usage of many next‐generation applications. These applications include a wide range of fields, such as healthcare, interactive gaming, video streaming, and other related technologies. Among such solutions are the evolving vehicular fog computing (VFC) frameworks that make use of IEEE and 3GPP protocols and use advanced optimization algorithms. However, these approaches often rely on outdated protocols or computationally intensive mathematical techniques for solving or representing their optimization models. Additionally, some of these frameworks have not thoroughly considered the type of application during their evaluation and validation phases. In response to these challenges, we have developed the “predictive analytics and modules” (PAM) framework, which operates on a time and event‐driven basis. It utilizes up‐to‐date 3GPP protocols to address the inherent unpredictability of VFC‐enabled distributed computing systems required in smart healthcare systems. Through a combination of a greedy heuristic approach and a distributed offloading architecture, PAM efficiently optimizes decisions related to task offloading and computation allocation. This is achieved through specialized algorithms that provide support to computationally weaker devices, all within a time frame of under 100 ms. To assess the performance of PAM in comparison to three benchmark methodologies, the evaluation pathways that we employed are average response time, probability density function, pareto‐analysis, algorithmic run time, and algorithmic complexity.

Behavior in Using Video Streaming Technology: Facilitating Conditions and Habits

The research was motivated by the phenomenon of active Twitch users in Indonesia, both as streamers and viewers, which had decreased from the previous few years. When compared to neighboring countries such as China, Japan and Korea, Indonesia has the lowest number of total users. The decrease in the number of users has an impact on Twitch's market penetration efforts into Indonesia and for streamers from Indonesia who are building their careers on the Twitch website. Research was conducted to obtain an overview of the level of acceptance of Twitch video streaming technology in Bandung. The use of the UTAUT2 model was implemented to obtain factors influencing the level of acceptance of Twitch usage. The research method uses quantitative, namely distributing online surveys from Google Form to the population and samples using the purposive sampling method. Distributing the questionnaire via social media then collecting the data in Microsoft Excel and SPSS for data processing. The research results show that the behavior of using Twitch video streaming is influenced by facilitating conditions and habits.

Website of the Health Office of PPKB (Population Control, and Family Planning) of Mojokerto City

The use of information technology has become mandatory in the current digital era, followed by the increasingly widespread development of the internet, which has further strengthened the importance of a web- based system. In designing a website-based system, the Department of Health, Population Control and Family Planning creates a Health Information System, PPKB Program, or Population Statistics. With this system, it explains the importance of the PPKB Health Strategy, in information related to population in an interesting and easy to understand way, detailed information regarding available health services, the PPKB (Population Control and Family Planning) program, population policies, and the latest initiatives. Tools or Interactives that help users in planning a family or understanding population statistics. Responsive design that ensures the website can be accessed well on various devices, including mobile phones and tablets, and ensures accessibility for users with special needs such as health services, information, or adequate accessibility features such as clear actions for visitors to engage further, or such as register for the PPKB program, look for further health information or can join relevant events or campaigns. Integrate social media, video streaming or other latest technology to improve user order and distribute information more effectively. This website must be a valuable and reliable source of information for the residents of the city of Mojokerto, providing needed support in health, PPKB programs, and understanding population issues. The success of this website depends on its ease of use, relevance of content, and its ability to encourage positive actions from visitors.

Pelatihan Penerapan Media Digital Streaming Video Menggunakan Aplikasi Vmix pada UPTD BTIKP Disdik Sumsel

Training on the application of digital media streaming video using the vMix application at the UPTD BTIKP of the South Sumatra Disdik is a strategic step to meet the demands of the times in educational development. Involving employees, the training aims to improve their understanding and skills in implementing video streaming technology. Using a structured method, including an introduction, demonstration of the vMix application, individual practical sessions, discussions, and presentation of results, the training successfully provided participants with in-depth understanding and technical skills. The results include improved employee competencies in vMix implementation, creating the basis for a more innovative and technologically-adaptive education transformation.

Future Hybrid Technology for Pay TV Platform

The main objective of this study is to analyze the potentiality of new technology of TV broadcasting and video distribution systems and how to adapt to the new trend of video viewing experience and design a future generation TV for the Bangladesh Pay TV industry. This research is based on Bangladesh and the interest of Bangladesh's Pay-TV industry delves into a novel approach aimed at mitigating these challenges by harnessing terrestrial or mobile networks for video broadcasting, thereby upgrading the old-fashioned broadcasting system or diminishing reliance on conventional internet-based streaming for mobile. This study adapts to the latest practice and implementation of ATSC 3.0 Terrestrial broadcasting and video streaming technology enhancement and business success along with emerging 5G network feasibility. ATSC 3.0 terrestrial broadcasting for mobile users has a significant impact on view experience, and seamless video transmission without the usage of internet data, especially in rural areas of the country where the 4G network is very limited. Local Gateway Node will receive ATSC 3.0 live TV and locally added offline video or streaming apps injected through API to deliver household through private CDN. This study offers a comprehensive technical feasibility, benefits, and implications of this innovative approach, upgradation of terrestrial broadcasting system highlighting its potential to revolutionize video delivery to mobile users. This study analyzes the technology evolution and adaptability with the recent trend of video content viewing experience, the importance of this study is significant for Bangladesh's pay-TV market to secure the future business.

Motion Detection Car using WiFi Cam and NodeMCU

Our project combines a WiFi camera and NodeMCU-controlled car to establish an intelligent surveillance system. The WiFi camera captures and streams real-time video to a server while a NodeMCU-controlled car autonomously responds to detected motion. When motion is identified, the system instructs the car to investigate the source, offering a dynamic approach to surveillance. This innovative solution presents cost-effective and flexible applications across diverse environments, including homes, offices, and outdoor spaces. It harnesses the power of WiFi technology for efficient video streaming and integrates motion-based control for enhanced security and responsiveness. The system's adaptability and real-time monitoring capabilities make it a practical choice for a wide range of surveillance needs, ensuring both convenience and peace of mind for users

VIDEO STREAMING PLATFORMS FOR LEARNING ENGLISH – CASE STUDY: NETFLIX

We live in an age of technology when people increasingly give up on television and turn to online streaming sources to entertain. These services provide an alternative to cable and satellite on-demand service, sometimes at a lower cost. Due to its intense development over the last few years, video streaming technology has attracted academic attention as a valuable tool for learning or improving English. Thus, this study discusses the practicality of video streaming use in foreign language higher education. It starts with a brief review of the literature on the use of video for language learning, focusing on the main benefits for learners. Then, the study presents the video as a learning tool underlying its advantages over the other previous physical technologies. To highlight the value of video streaming to language education, benefits and problems are explained, and Netflix is outlined as an example of a streaming video platform that may be used within English language teaching. General vocabulary items will be acquired, and listening skills will be improved. However, the interest in studying and approaching new topics focusing on specialized vocabulary will be recognized as an advantage when utilizing video streaming platforms. Next, specific strategies for using video streaming platforms will be outlined. The first was that short documentaries could be watched along with multiple-choice questions to promote general vocabulary building. The second is that Netflix’s series or movies can be utilized to find specific themes to help explain particular grammar or vocabulary points while improving the specialized business vocabulary.

A Visual Sensitivity Aware ABR Algorithm for DASH via Deep Reinforcement Learning

In order to cope with the fluctuation of network bandwidth and provide smooth video services, adaptive video streaming technology is proposed. In particular, the adaptive bitrate (ABR) algorithm is widely used in dynamic adaptive streaming over HTTP (DASH) to improve quality of experience (QoE). However, existing ABR algorithms still ignore the inherent visual sensitivity of human visual system (HVS). As the final receiver of video, HVS has different sensitivity to the quality distortion of different video content, and video content with high visual sensitivity needs to allocate more bitrate resources. Therefore, existing ABR algorithms still have limitations in reasonably allocating bitrate and maximizing QoE. To solve this problem, this paper designs an adaptive bitrate strategy from the perspective of user vision, studies the modeling of visual sensitivity, and proposes a visual sensitivity aware ABR algorithm. We extract a set of content features and attribute features from the video, and consider the simulation of HVS to establish a total masking effect model that reflects the visual sensitivity more accurately. Further, the network status, buffer occupancy, and visual sensitivity are comprehensively considered under a deep reinforcement learning framework to select the appropriate bitrate for maximizing QoE. We implement the proposed algorithm over a realistic trace-driven evaluation and compare its performance with several latest algorithms. Experimental results show that our algorithm can align ABR strategy with visual sensitivity to achieve better QoE in high visual sensitivity content, and improves the average perceptual video quality and overall user QoE by 18.3% and 22.8%, respectively. Additionally, we prove the feasibility of our algorithm through subjective evaluation in the real environment.

A Framework for Multi-Task Learning in Dynamic Adaptive Streaming Over HTTP

This paper presents a framework with a taxonomy for multi-task learning in the context of Dynamic Adaptive Streaming over HTTP (DASH). DASH is a widely used technology for video streaming, and multi-task learning has emerged as a promising approach to enhance the performance and user experience of DASH systems by jointly optimizing multiple related tasks. The framework provides a structured approach to design, train, and evaluate multi-task learning models in DASH, while the taxonomy categorizes the key components and approaches within the framework. The taxonomy includes task types, multitask learning approaches, input features, and training strategies. Task types encompass video quality adaptation, buffer management, bandwidth estimation, content pre-fetching, and resource allocation, representing the specific tasks involved in DASH. Multi-task learning approaches encompass methodologies such as shared representation learning, task-specific layers, multi-head architectures, knowledge distillation, and reinforcement learning, offering flexibility in model design and optimization. Input features cover video characteristics, network conditions, device capabilities, and user preferences, providing the necessary information for informed decision-making across tasks. Training strategies include joint training, alternate training, hierarchical training, task weighting, and task balancing, determining how the multi-task learning model is trained and optimized. By following the presented framework and taxonomy, researchers and practitioners can systematically approach the design, training, and evaluation of multi-task learning models in DASH. The framework enables the development of efficient and adaptive video streaming systems by leveraging the interdependencies among tasks. The taxonomy helps organize the components and approaches within the framework, aiding in a better understanding of the various aspects of multi-task learning in the DASH context. Overall, this framework and taxonomy provide a valuable resource for advancing the field of multitask learning in the dynamic and complex domain of video streaming over HTTP.

Parental experiences of live video streaming technology in neonatal care in England: a qualitative study

BackgroundThe use of bedside cameras in neonatal units facilitates livestreaming of infants to support parental and family bonding when they are unable to be physically present with their baby. This study aimed to explore the experiences of parents of infants previously admitted for neonatal care and who used live video streaming to view their baby in real-time.MethodsQualitative semi-structured interviews were conducted after discharge with parents of infants admitted for neonatal care on a tertiary level neonatal unit in the UK in 2021. Interviews were conducted virtually, transcribed verbatim and uploaded into NVivo V12 to facilitate analysis. Thematic analysis by two independent researchers was undertaken to identify themes representing the data.ResultsSeventeen participants took part in sixteen interviews. Thematic analysis identified 8 basic themes which were grouped into 3 organizational themes: (1) family integration of the baby including parent-infant, sibling-infant, and wider family-infant attachment facilitated through livestreaming, (2) implementation of the livestreaming service including communication, initial set up of the livestreaming service, and areas for improvement, and (3) parental control including emotional, and situational control.ConclusionsThe use of livestreaming technology can provide parents with opportunities to integrate their baby into their wider family and friendship community and gain a sense of control over their baby’s admission for neonatal care. On-going parental education around how to use, and what to expect from, livestreaming technology is required to minimise any potential distress from viewing their baby online.

Deep Reinforcement Learning-based Bitrate Adaptations in Dynamic Adaptive Streaming over HTTP

Dynamic adaptive streaming over HTTP (DASH) has been a superior video streaming technology in recent years. Bitrate adaptation function at video player plays a vital role in guaranteeing a high quality-of-experience for the users. This work evaluates the performance of several advanced deep reinforcement learning algorithms, i.e., deep Q-learning, actor-critic, and proximal policy optimization, applied in bitrate adaptations and compares them with other rate adaptation methods with real-trace datasets.

Technological affordances of video streaming platforms: Why people prefer video streaming platforms over television

This article investigates to what extent technological affordances are associated with people's preference for video streaming platforms over traditional television services. Such affordances refer to properties of these platforms (including personalized recommendations and easy-to-navigate interfaces) that provoke certain uses of the technology and satisfy social and psychological needs. Based on a quantitative study of 25–50-year-olds in Belgium ( N = 596) and a hierarchical regression analysis, the study builds further on the conceptualization of technological affordances as presented in the MAIN model, which suggests that four affordances (Modality, Agency, Interactivity and Navigability) are central to digital media technology. As such, the study presents an affordance-based measure of video streaming platforms, and helps to understand how video streaming technology shapes new patterns of audiovisual consumption and enhances the viewing experience beyond that of traditional television. Whereas most research attention has focused on user-oriented gratifications of video streaming platforms, this study addresses a gap in the literature by dealing with platform-oriented gratifications of video streaming platforms.

ANALYSIS OF VIDEO DATA COMPRESSION ALGORITHMS AND OPTIMIZED FOR USE IN REMOTELY CONTROLLED DRONES

Due to the widespread use of unmanned aerial vehicles (UAVs) in the civil sphere, there is a need to improve the video image generation quality and transmission technologies, changing the video streams coding to reduce their size and improve quality. Video streaming technologies are moving further towards complicating the video transmission systems used, which helps improve the received video data quality. Higher of transmitted video signal quality – more requirements for frequency band increase used. This fact leads us to use effective video compression algorithms that allow us to combine high image quality with a narrow bandwidth of frequencies used. In this paper, currently existing coding algorithms, their efficiency and computational complexity are studied. Among the algorithms under consideration there will only be those whose effectiveness has been proven by finding their application in modern realities. Based on the research, conclusions will be drawn regarding the feasibility of using each encoding algorithm for transmitting video data in real time.

Streaming Video Alliance Progress Report About the Streaming Video Alliance

Comprised of members from across the video ecosystem, the Streaming Video Technology Alliance (SVTA) is a global association that works to solve critical streaming video challenges to improve end-user experience and adoption. The organization focuses on three main activities. The first is to educate the industry on challenges, technologies, and trends through informative, publicly available resources such as whitepapers, articles, and e-books. The second is to foster collaboration among different video ecosystem players through working groups, quarterly meetings, and conferences. The third is to define solutions for streaming video challenges by producing specifications, best practices, and other technical documentation.

DASH Framework Using Machine Learning Techniques and Security Controls

Interest in video streaming has increased recently, as it constitutes most of the traffic on the Internet and cellular networks. These networks use different video streaming technologies. One of the most famous technologies is DASH (which stands for Dynamic Adaptive Steaming using HTTP). DASH adapts streaming parameters according to network conditions and uses the HTTP protocol to communicate between the user and the server. DASH faces many challenges that may lead to video interruptions and poor quality of user experiences (QoE) such as bad network conditions and buffering level control. In addition to the lack of studies, we cover security issues for these types of services. In this paper, we proposed an integrated framework that consists of four components: quality prediction model, precache model, light web application firewall, and a monitoring system. These four components improve QoE and precache and increase the level of security. The results of the quality prediction model are used to predict the quality of the next segments depending on the user’s network conditions and in the precache model to improve caching to reduce the load on the streaming system and rely more on cache servers. The proposed web application firewall is a light version used to defend against video streaming attacks and verify the existence of necessary HTTP headers. The quality predictor model with the generated dataset achieved 97% classification accuracy using DecisionTree, and this experiment proved the strong relationship between congestion periods and streaming quality, which is s the main key in QoE.

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.

Optimizing Immersive Video Coding Configurations Using Deep Learning: A Case Study on TMIV

Immersive video streaming technologies improve Virtual Reality (VR) user experience by providing users more intuitive ways to move in simulated worlds, e.g., with 6 Degree-of-Freedom (6DoF) interaction mode. A naive method to achieve 6DoF is deploying cameras at numerous different positions and orientations that may be required based on users’ movement, which unfortunately is expensive, tedious, and inefficient. A better solution for realizing 6DoF interactions is to synthesize target views on-the-fly from a limited number of source views. While such view synthesis is enabled by the recent Test Model for Immersive Video (TMIV) codec, TMIV dictates manually-composed configurations, which cannot exercise the tradeoff among video quality, decoding time, and bandwidth consumption. In this article, we study the limitation of TMIV and solve its configuration optimization problem by searching for the optimal configuration in a huge configuration space. We first identify the critical parameters in the TMIV configurations. Then, we introduce two Neural Network (NN) -based algorithms from two heterogeneous aspects: (i) a Convolutional Neural Network (CNN) algorithm solving a regression problem and (ii) a Deep Reinforcement Learning (DRL) algorithm solving a decision making problem, respectively. We conduct both objective and subjective experiments to evaluate the CNN and DRL algorithms on two diverse datasets: an equirectangular and a perspective projection dataset. The objective evaluations reveal that both algorithms significantly outperform the default configurations. In particular, with the equirectangular (perspective) projection dataset, the proposed algorithms only require 95% (23%) decoding time, stream 79% (23%) views, and improve the utility by 6% (73%) on average. The subjective evaluations confirm the proposed algorithms consume fewer resources while achieving comparable Quality of Experience (QoE) than the default and the optimal TMIV configurations.

Fingerprinting Technique for YouTube Videos Identification in Network Traffic

Recently, many video streaming services, such as YouTube, Twitch, and Facebook, have contributed to video streaming traffic, leading to the possibility of streaming unwanted and inappropriate content to minors or individuals at workplaces. Therefore, monitoring such content is necessary. Although the video traffic is encrypted, several studies have proposed techniques using traffic data to decipher users’ activity on the web. Dynamic Adaptive Streaming over HTTP (DASH) uses Variable Bit-Rate (VBR) - the most widely adopted video streaming technology, to ensure smooth streaming. VBR causes inconsistencies in video identification in most research. This research proposes a fingerprinting method to accommodate for VBR inconsistencies. First, bytes per second (BPS) are extracted from the YouTube video stream. Bytes per Period (BPP) are generated from the BPS, and then fingerprints are generated from these BPPs. Furthermore, a Convolutional Neural Network (CNN) is optimized through experiments. The resulting CNN is used to detect YouTube streams over VPN, Non-VPN, and a combination of both VPN and Non-VPN network traffic.