Video Streaming Research Articles

COMPARISON OF QUALITY OF SERVICE PARAMETERS FOR FOUR CELLULAR NETWORK PROVIDERS IN PUNGGUR KECIL VILLAGE

This research aims to analyze Quality of Service (QoS) parameters on cellular networks using four providers by measuring throughput, packet loss, delay and jitter parameters when conducting Video Streaming and Online Games using the Wireshark application. The research method involves direct measurement of QoS parameters in several strategic locations in Punggur Kecil village, data was taken at three different location points with a distance comparison of ±500 meters. From all the recapitulations of QoS parameter measurement results, it can be concluded that the value of each QoS parameter can vary for each package which includes the categories poor, medium, good and very good because the bandwidth received by each provider is different. The cause of high and low values of different QoS parameters, Throughput is caused by the number of packet arrivals measured during the observation process, Packet Loss is caused by the number of queues exceeding capacity, Delay is caused by low Throughput values and the number of packets sent, while the Jitter value is influenced by the Delay value. The results obtained are that the Indosat provider is the recommended provider when carrying out Video Streaming and Online Games, because the Quality of Service value produced is superior to other providers. The average values of the Throughput, Packet Loss, Delay and Jitter parameters at each location for the Indosat provider are respectively 796kbps, 2.63%, 22.6ms and 22.6ms. Provider XL is 708kbps, 1.67%, 12.6ms, and 12.6ms. Provider Telkomsel 667kbps, 0.33%. 55.9ms, and 56ms. As well as the Smartfren provider 50kbps, 0.38%, 102.6ms, and 102.7ms.

HoloGCS: mixed reality-based ground control station for unmanned aerial vehicle

Human–robot interaction (HRI), which studies the interaction between robots and humans, appears as a promising research idea for the future of smart factories. In this study, HoloLens as ground control station (HoloGCS) is implemented, and its performance is discussed. HoloGCS is a mixed reality-based system for controlling and monitoring unmanned aerial vehicles (UAV). The system incorporates HRI through speech commands and video streaming, enabling UAV teleoperation. HoloGCS provides a user interface that allows operators to monitor and control the UAV easily. To demonstrate the feasibility of the proposed systems, a user case study (user testing and SUS-based questionnaire) was performed to gather qualitative results. In addition, throughput, RTT, latency, and speech accuracy were also gathered and analyzed to evaluate quantitative results.

Paintings in naked-eye virtual reality: a parallax view between the surface and volumetric depth

This article explores a subgenre of naked-eye virtual reality (VR) video that features two-dimensional paintings in a three-dimensional space and is circulated on the Chinese video streaming website Bilibili. In contrast to traditional VR, which requires the spectator to wear a head-mounted display, naked-eye VR offers a stereoscopic view on the screen, eliminating the need for VR glasses. The apparent incongruity between the screen’s physical flatness and the volumetric depth of the painting becomes even more pronounced when the naked-eye VR image emphasises the painting’s inherent two-dimensionality. Employing an interdisciplinary humanities’ method that connects media archaeology, film studies, art history, and the field of science, technology, and society, this paper delves into this distinctive juxtaposition between the painting’s flatness and the volumetric depth characteristic of naked-eye VR, a subgenre that remains mostly uncharted in the field of film and media studies. Taking a media archaeological perspective, this paper introduces a parallax media history, suggesting that the aesthetics of VR can be traced not only to the stereoscope but also to scroll paintings, which are paradoxically defined by their flat compositions. In addition, naked-eye VR references pictorial traditions by generating a spatial illusion that leads spectators to feel as if they are delving beneath the surface of a painting, transitioning from spatial extensivity on the x-y axis to perceived depth along the z-axis. This oscillation between surface and depth is engendered by the horizontal parallax rooted in binocular disparity, thereby positioning naked-eye VR as a “parallax media.” In analysing the illusion created by naked-eye VR, this paper proposes a paradigmatic shift in the framework of media study from conceiving of an image as a mirror to experiencing it as a door, representing the oscillation between volumetric depth and two-dimensional surface.

Advancements and Challenges in 360° Augmented Reality Video Streaming: A Comprehensive Review

As the demand for immersive multimedia experiences continues to grow, 360° augmented reality (AR) video streaming has emerged as a promising technology to deliver interactive and engaging content to users. This review paper provides a comprehensive overview of the current state of 360° AR video streaming, focusing on recent advancements, challenges, and potential future directions. The paper begins by presenting an in-depth exploration of the fundamental concepts and technologies underpinning 360° AR video streaming, including camera capture techniques, video encoding and compression methods, and the integration of augmented reality overlays. Subsequently, it delves into a critical analysis of the existing literature, highlighting key developments in the field. This encompasses advancements in video quality, real-time rendering, and the integration of interactive AR elements within the 360° video environment. Furthermore, the review addresses the challenges and limitations associated with 360° AR video streaming, ranging from bandwidth constraints and latency issues to hardware requirements and user experience considerations. Solutions and strategies proposed in the literature to mitigate these challenges are discussed, providing insights into the ongoing research efforts aimed at enhancing the overall performance and user satisfaction. In addition to technical aspects, the paper explores diverse applications of 360° AR video streaming across various domains, such as entertainment, education, healthcare, and industry. This section highlights successful implementations, innovative use cases, and potential benefits realized in each domain. The review concludes by outlining potential future trends and research directions in the dynamic field of 360° AR video streaming. Anticipated developments in hardware, software, and content creation are discussed, along with their potential impact on the widespread adoption of this technology. By synthesizing and critically examining the existing body of knowledge, this review paper serves as a valuable resource for researchers, practitioners, and industry professionals interested in gaining a comprehensive understanding of the current landscape, challenges, and opportunities in 360° augmented reality video streaming.

Evolution of CNNs in human tracking applications

Tracking the movement of objects in videos or footage from CCTV systems plays an integral role in crime investigations, surveillance, security predictions, and many other domains. Historically, this task was primarily entrusted to dedicated observers or analysts who would be summoned to review pre-recorded footage post-event. With the advent of machine learning and AI, convolutional neural networks (CNNs) have paved the way for computers to augment human capabilities in analyzing streaming videos or archived recordings. Among the various tracking methodologies that machine learning offers, YOLO (You Only Look Once) and R-CNN (Region-based Convolutional Neural Networks), along with its iterations, stand out as some of the most reliable and precise. However, the scope of analysis often extends beyond these techniques. To enhance accuracy and provide an adept classification mechanism, the Deep SORT (Simple Online and Realtime Tracking) algorithm emerges as pivotal. Its synergy with human detection remains a significant area of discussion and will be deliberated upon in this study. This review aims to elucidate the intricacies of these state-of-the-art methods and their interplay in modern tracking systems.

A Survey on Video Streaming for Next-Generation Vehicular Networks

As assisted driving technology advances and vehicle entertainment systems rapidly develop, future vehicles will become mobile cinemas, where passengers can use various multimedia applications in the car. In recent years, the progress in multimedia technology has given rise to immersive video experiences. In addition to conventional 2D videos, 360° videos are gaining popularity, and volumetric videos, which can offer users a better immersive experience, have been discussed. However, these applications place high demands on network capabilities, leading to a dependence on next-generation wireless communication technology to address network bottlenecks. Therefore, this study provides an exhaustive overview of the latest advancements in video streaming over vehicular networks. First, we introduce related work and background knowledge, and provide an overview of recent developments in vehicular networking and video types. Next, we detail various video processing technologies, including the latest released standards. Detailed explanations are provided for network strategies and wireless communication technologies that can optimize video transmission in vehicular networks, paying special attention to the relevant literature regarding the current development of 6G technology that is applied to vehicle communication. Finally, we proposed future research directions and challenges. Building upon the technologies introduced in this paper and considering diverse applications, we suggest a suitable vehicular network architecture for next-generation video transmission.

Exploring the Prospect of Over-the-Top (OTT) Platforms in Bangladesh: A Study on the Users of Khulna District

Introduction: Over-the-Top (OTT) services are developing rapidly in line with the technolo gies’ fast advancement. With the emergence of OTT platforms, the viewing experience has seen a drastic change. OTT platforms are becoming more attractive to viewers due to their diversity of content and less advertisements. The main purpose of this study is to understand the perception and satisfaction of the OTT users of Khulna District. This study also aims to investigate the OTT video streaming habits and preferences of the users. Methods: This study is based on a survey method to know the viewer’s engagement, perception and satisfaction using OTT platforms. Using the convenience sampling technique, an online survey is admin istered to collect feedback from 200 respondents. Results: The findings of this study revealed that 48.5% people prefer OTT platforms as their major source of entertainment to television or other video streaming platforms. According to the study, Chorki is the viewer’s most popular OTT video streaming service. A majority of OTT subscribers thought that the subscription fees in Bangladesh are higher (42.5%). Censorship is required for semi-nude or sex-based stories in OTT platforms, according to the majority of the users (47%). Therefore, majority of the sub scribers are not satisfied with the content of these platforms (34%). Most of them (49%) faced technical difficulties while using OTT platforms. Conclusion: In Bangladesh, OTT platforms have enormous potentials. As the market for these services are expanding swiftly and investors are recognizing its protectives. This study is expected to help OTT service providers in design ing their contents and services.

Effective hybrid video denoising and blending framework for Internet of Remote Things (IoRT) environments

The Internet of Remote Things (IoRT) has emerged as a transformative paradigm, merging IoT capabilities with remote technologies. IoRT environments, featuring interconnected sensors and robots, face challenges like sensor noise and low-light conditions, compromising video stream quality. This paper proposes a Hybrid Video Denoising and Blending Framework to address IoRT video data shortcomings. Leveraging spatial and temporal domain denoising techniques, the framework effectively removes noise while preserving crucial details. The inclusion of advanced blending algorithms facilitates seamless fusion of data from multiple sources, enhancing decision-making in real-world scenarios. The framework adopts a dynamic weighted averaging approach and an optimal sensor selection mechanism to intelligently choose informative data sources, improving blended output quality. Extensive experiments with a diverse IoRT dataset showcase the framework's superiority over state-of-the-art techniques, offering significant enhancements in video quality, noise reduction, and data fusion accuracy. Applications like surveillance, autonomous remotes, and industrial automation can benefit from the framework's ability to provide clearer, more reliable visual information. In conclusion, this research introduces a pioneering approach to mitigate video noise and enhance data fusion in IoRT, showcasing promising results and paving the way for further research in the integration of Remotes and IoT.

EMS: Erasure-Coded Multi-Source Streaming for UHD Videos Within Cloud Native 5G Networks

Ultra-High-Definition (UHD) videos have been getting increasing attention. However, existing video streaming solutions fail to deliver them due to the extremely high bandwidth requirement. The emerging cloud native 5G networks have opened up the possibility of enhancing UHD video quality by leveraging in-network video streaming. Unfortunately, the restricted storage and bandwidth of in-network servers could become the main bottleneck. To this end, we present <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf EMS}$</tex-math></inline-formula> , a novel UHD video streaming framework, by integrating <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</u> rasure-coded storage with <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</u> ulti-source <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> treaming. We respectively introduce a deadline-aware and a latency-sensitive metric to indicate the service quality of video servers and advocate a federated learning paradigm for the adaptive service quality update, including a reinforcement learning based multi-server selection (i.e., user local training) and a global service quality aggregation. To facilitate user local training without sacrificing streaming Quality-of-Experience (QoE), we cast the multi-server selection associated with the restriction on the average number of selected servers per video chunk into two kinds of Multi-Armed Bandit (MAB) models in terms of the proposed service quality metrics. We design lightweight Upper Confidence Bound (UCB) based algorithms with a theoretical performance guarantee. We implement a prototype of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf EMS}$</tex-math></inline-formula> , and extensive experiments confirm the superiority of the proposed algorithms.

Health Education Method on Leprosy Prevention: Integrative Review.

Leprosy is still found mainly in lower-middle-income countries. Breaking the chain of leprosy transmission requires various ways, especially by increasing knowledge of leprosy prevention through health education. Although several studies have mentioned the effectiveness of health education, there is still uncertainty about the best method to use. Therefore, the review question should be answered: what forms of health education have been developed about Leprosy in the community? This review study complies with the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) version 2020. The search for relevant literature involved PubMed, ScienceDirect, ERIC, Cochrane, and ProQuest databases for the studies published in the 2000 to 2023 period with the inclusion criteria such as increasing knowledge, changing attitudes and behavior, and increasing positive stigma. The quality of the study was assessed using the Critical Appraisal Skills Program, and the Risk of Bias using the Cochrane RoB tool. Overall search on databases resulted in a total of 184,681 articles. Only six studies were eligible to be included in this review. Various educational methods were used in the eligible studies, including MH Mobile, the myth or truth on Leprosy game, posters, leaflets, community meetings, tele-education theweb, discussion lists, chats, activity diaries, iconographic 3D videos, classes on video streaming, video conference, case simulation, lecture, and the contact intervention (education, testimonies (direct contact), videos, and comics). All of the methods used had the potential to improve knowledge, attitude, and practice and reduce negative stigma regarding Leprosy. There is no method of health education superior to another on Leprosy prevention.

Real-time mask-wearing detection in video streams using deep convolutional neural networks for face recognition

This research aims to develop a real-time mask-wearing detection system using deep convolutional neural networks (CNNs). This is crucial in the coronavirus disease 2019 (COVID-19) pandemic to alert individuals who are not wearing masks early on, thereby reducing the spread of the virus. Since COVID-19 primarily spreads through respiratory droplets and mask-wearing is recommended, our proposed study utilizes computer vision techniques, specifically image processing, to detect masked and unmasked faces. We employ a customized CNN architecture consisting of five convolutional layers, followed by max-pooling layers and fully connected (FC) layers. The final output layer utilizes softmax activation for classification. The model is updated with optimized layer configurations and parameter values. We are developing an application that uses a digital camera as an input device. The application utilizes a dataset comprising 11,792 image samples, which are used for training and testing purposes with the 80:20 ratio. Real-time testing is conducted using human subjects captured by the camera. The experimental results demonstrate that the CNN method achieves a classification accuracy of 99% on the training data and 98.83% during real-time video testing. These findings suggest that the real-time mask detection system using CNN performs effectively.

Cross-Modal Stream Transmission: Architecture, Strategy, and Technology

Since the multi-modal services, which elaborately integrate audio, video, and haptic streams, are able to substantially improve the users' immersive experience, they are gradually becoming the mainstream applications. However, the existing solutions fail to simultaneously satisfy the diverse transmission requests of multi-modal services in terms of lower latency, higher reliability, and higher throughput, especially in the resource-constrained and dynamic environment. To circumvent this dilemma, this work constructs a cross-modal stream transmission architecture by exploring the intra-modal redundancy and the inter-modal correlation. Specifically, we propose an adaptive stream scheduling strategy, which can not only substantially decrease the stream traffics, but also dramatically reduce the impact of the channel uncertainty and the haptic signals' stochastic arrival on the delay jitter and reliability. Moreover, through prioritizing the resource provision of haptic signals, we design a joint uplink and downlink resource allocation scheme based on the prediction model. In particular, the transmitter sends the predicted haptic signals via the allocated resource blocks in advance according to the optimal window size, so as to offset the latency and remain the symmetry during the bilateral haptic transmission. Meanwhile, we develop an audio and video stream transmission scheme in a hierarchical and reusing structure by integrating the power-domain NOMA with LMDC-FEC, which fully utilizes the remaining limited resources to maximize the robustness of audio-video transmission.

Joint Optimization of QoE and Fairness for Adaptive Video Streaming in Heterogeneous Mobile Environments

The rapid growth of mobile video traffic and user demand poses a more stringent requirement for efficient bandwidth allocation in mobile networks where multiple users may share a bottleneck link. This provides content providers an opportunity to jointly optimize multiple users’ experiences but users often suffer short connection durations and frequent handoffs because of their high mobility. In this paper, we propose an end-to-end scheme, VSiM, for supporting mobile video streaming applications in heterogeneous wireless networks. The key idea is allocating bottleneck bandwidth among multiple users based on their mobility profiles and Quality of Experience (QoE)-related knowledge to achieve max-min QoE fairness. Besides, the QoE of buffer-sensitive clients is further improved by the novel server push strategy based on HTTP/3 protocol without affecting the existing bandwidth allocation approach or sacrificing other clients’ view quality. VSiM is lightweight and easy to deploy in the real world without touching the underlying network infrastructure. We evaluated VSiM experimentally in both simulations and a lab testbed on top of the HTTP/3 protocol. We find that the clients’ QoE fairness of VSiM achieves more than 40% improvement compared with state-of-the-art solutions, i.e., the viewing quality of clients in VSiM can be improved from 720p to 1080p in resolution. Meanwhile, VSiM provides about 20% improvement of average QoE.

Vehicle Detection in High Density Traffic Surveillance Data using YOLO.v5

Abstract: Computer vision is one of the prime domains that enable to derive meaningful and crisp information from digital media, such as images, videos, and other visual inputs. Background: Detection and correctly tracking the moving objects in a video streaming is still a challenging problem in India. Due to the high density of vehicles, it is difficult to identify the correct objects on the roads. Methods: In this work, we have used a YOLO.v5 (You Only Look Once) algorithm to identify the different objects on road, such as trucks, cars, trams, and vans. YOLO.v5 is the latest algorithm in the family of YOLO. To train the YOLO.v5, KITTY dataset was used having 11682 images having different objects in a traffic surveillance system. After training and validating the dataset, three different models have been constructed setting various parameters. To further validate the proposed approach, results have also been evaluated on the Indian traffic dataset DATS_2022. Results: All the models have been evaluated using three performance metrics, such as precision, recall, and mean average precision (MAP). The final model has attained the best performance on KITTY dataset as 93.5% precision, 90.7% recall, and 0.67 MAP for different objects. The results attained on the Indian traffic dataset DATS_2022 included 0.65 precision, 0.78 recall value, and 0.74 MAP for different objects. Conclusion: The results depict the proposed model to have improved results as compared to stateof-the-art approaches in terms of performance and also reduce the computation time and object loss.

Online Unsupervised Video Object Segmentation via Contrastive Motion Clustering

Online unsupervised video object segmentation (UVOS) uses the previous frames as its input to automatically separate the primary object(s) from a streaming video without using any further manual annotation. A major challenge is that the model has no access to the future and must rely solely on the history, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., the segmentation mask is predicted from the current frame as soon as it is captured. In this work, a novel contrastive motion clustering algorithm with an optical flow as its input is proposed for the online UVOS by exploiting the common fate principle that visual elements tend to be perceived as a group if they possess the same motion pattern. We build a simple and effective auto-encoder to iteratively summarize non-learnable prototypical bases for the motion pattern, while the bases in turn help learn the representation of the embedding network. Further, a contrastive learning strategy based on a boundary prior is developed to improve foreground and background feature discrimination in the representation learning stage. The proposed algorithm can be optimized on arbitrarily-scale data ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., frame, clip, dataset) and performed in an online fashion. Experiments on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DAVIS₁₆, FBMS</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SegTrackV2</i> datasets show that the accuracy of our method surpasses the previous state-of-the-art (SoTA) online UVOS method by a margin of 0.8%, 2.9%, and 1.1%, respectively. Furthermore, by using an online deep subspace clustering to tackle the motion grouping, our method is able to achieve higher accuracy at 3× faster inference time compared to SoTA online UVOS method, and making a good trade-off between effectiveness and efficiency. Our code is available at https://github.com/xilin1991/CluterNet.

Remote physics experiment Mathematical pendulum as an attractive alternative to traditional laboratory exercises

Traditional laboratory exercises are usually not popular among students, especially the processing and submission of protocols. Students are used to using digital technologies, prefer active participation in the learning process, and love visualisations. To improve motivation and make laboratory practices more attractive to students, remote laboratories with the real time control and available anytime and anywhere can be offered. In this paper, we present the remote experiment Mathematical Pendulum, which is physically located at the Department of Physics, Faculty of Science, Jan Evangelista Purkyně University (UJEP) in Ústí nad Labem. Using it we can study physical laws such as the dependence of the pendulum’s period of oscillation on its length and determine the value of the gravitational acceleration. The remote pendulum is interactively controlled through a web browser from a computer or mobile phone, provides live video stream and experimental data downloadable via Internet. This single remote laboratory task is complemented by remote measurements of the acceleration of gravity at different latitudes through a group of ten identical mathematical pendulums created within the WorldPendulum WP@ELAB project. In this paper, we present not only the results of the measurements made, but also a pilot investigation of the use of this complex remote experiment.

ViChaser: Chase Your Viewpoint for Live Video Streaming With Block-Oriented Super-Resolution

ViChaser: Chase Your Viewpoint for Live Video Streaming With Block-Oriented Super-Resolution

Joint Request Updating and Elastic Resource Provisioning With QoS Guarantee in Clouds

In a commercial cloud, service providers (e.g., video streaming service provider) rent resources from cloud vendors (e.g., Google Cloud Platform) and provide services to cloud users, making a profit from the price gap. Cloud users acquire services by forwarding their requests to corresponding servers. In practice, as a common scenario, traffic dynamics will cause server overload or load-unbalancing. Existing works mainly deal with the problem by two methods: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">elastic resource provisioning</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">request updating</i> . Elastic resource provisioning is a fast and agile solution but may cost too much since service providers need to buy extra resources from cloud vendors. Though request updating is a free solution, it will cause a significant delay, resulting in a bad users’ QoS. In this paper, we present a new scheme, called real-time request updating with elastic resource provisioning (TRUST), to help service providers pay less cost with users’ QoS guarantee in clouds. In addition, we propose an efficient algorithm for TRUST with a bounded approximation factor based on progressive-rounding. Both small-scale experiment results and large-scale simulation results show the superior performance of our proposed algorithm compared with state-of-the-art benchmarks.

Aerial Video Streaming Over 3D Cellular Networks: An Environment and Channel Knowledge Map Approach

Aerial Video Streaming Over 3D Cellular Networks: An Environment and Channel Knowledge Map Approach

Captionomaly: A Deep Learning Toolbox for Anomaly Captioning in Social Surveillance Systems

Real-time video stream monitoring is gaining huge attention lately with an effort to fully automate this process. On the other hand, reporting can be a tedious task, requiring manual inspection of several hours of daily clippings. Errors are likely to occur because of the repetitive nature of the task causing mental strain on operators. There is a need for an automated system that is capable of real-time video stream monitoring in social systems and reporting them. In this article, we provide a tool aiming to automate the process of anomaly detection and reporting. We combine anomaly detection and video captioning models to create a pipeline for anomaly reporting in descriptive form. A new set of labels by creating descriptive captions for the videos collected from the UCF-Crime (University of Central Florida-Crime) dataset has been formulated. The anomaly detection model is trained on the UCF-Crime, and the captioning model is trained with the newly created labeled set UCF-Crime video description (UCFC-VD). The tool will be used for performing the combined task of anomaly detection and captioning. Automated anomaly captioning would be useful in the efficient reporting of video surveillance data in different social scenarios. Several testing and evaluation techniques were performed. Source code and dataset: https://github.com/Adit31/Captionomaly-Deep-Learning-Toolbox-for-Anomaly-Captioning.