Video Streaming Research Articles

The Effect of Live Video Streaming and e-WOM on Purchasing Decisions of Tik Tok Fashion Consumers with Trust as a Mediating Variable in Pekanbaru

The Effect of Live Video Streaming and e-WOM on Purchasing Decisions of Tik Tok Fashion Consumers with Trust as a Mediating Variable in Pekanbaru

DRL approach for online user-centric QoS-Aware SFC embedding with dynamic VNF placement

In this paper, we tackle online Service Function Chaining (SFC) embedding issue in Software Defined Network (SDN)/Network Function Virtualization (NFV) enabled networks using Deep Reinforcement Learning (DRL) approach. This approach investigates the problem of Quality of Experience (QoE)/Quality of Service (QoS) driven SFC embedding with dynamic Virtual Network Functions (VNF) placement. We particularly focus on video streaming traffic requiring a given Mean Opinion Score (MOS) quantified in terms of QoE. In this regard, we propose a DRL algorithm based on Deep-Q-Network (DQN) (named DQN_QoE/QoS_SFC algorithm) implementing an optimization problem maximizing QoE while meeting QoS requirements. Our algorithm named DQN_QoE/QoS_SFC improves previous work given in Chen et al. (2018) dealing with the same issue through bringing performance evaluation of the training phase by testing the learning quality. This is achieved through using a QoE Threshold Score (QoETh−Sc) to be attained on average by the DQN_QoE/QoS_SFC agent along the last 100 runs of the training phase while maximizing the expected cumulative reward. We conduct simulation experiments through two performance metrics related to the SFC request namely QoE (QoE) and Rejection Ratio (RR). We investigate also how these metrics evolve for the proposed algorithm when compared with two standard baseline algorithms (Violent search, Random search) during learning phase to achieve satisfactory training quality. We highlight how DQN_QoE/QoS_SFC algorithm behaves along the training process and how it attempts reaching a near-optimal solution as closer as possible to the optimal solution provided by Violent search algorithm.

RT3C: Real-Time Crowd Counting in Multi-Scene Video Streams via Cloud-Edge-Device Collaboration

RT3C: Real-Time Crowd Counting in Multi-Scene Video Streams via Cloud-Edge-Device Collaboration

DSJA: Distributed Server-Driven Joint Route Scheduling and Streaming Adaptation for Multi-Party Realtime Video Streaming

DSJA: Distributed Server-Driven Joint Route Scheduling and Streaming Adaptation for Multi-Party Realtime Video Streaming

The Effect of Video Streaming With Virtual Reality Glasses on on Pain Anxiety and Satisfaction Applied in Peripheral Intravenous Catheter Process

Peripheral intravenous catheter (PIC) implementation is among the most frequent and highly invasive nursing initiatives. PIC leads to anxiety with procedural pain, causing individuals to reject these procedures or negatively affecting the process's success. The study was conducted to determine the effect of virtual reality glasses (VRG) on pain, anxiety, and patient satisfaction during the peripheral intravenous catheter (PIC) process in adults. An experimental study. An emergency department of a university hospital. Individuals who were admitted to the emergency unit of the university hospital were recruited between the data collection dates. Data from individuals' "Patient Information Form," "Visual Pain Scale," "Visual Anxiety Scale," and "Visual Satisfaction Scale" were collected. "VR-Box 3D Glasses" was used as virtual reality glasses. In our study, when the pain point averages with VAS were compared during the PIC process, the pain score average of the patients in the control group was 5.78 ± 1.23 visual anxiety scale 5.89 ± 1.23 and patient satisfaction scale 3.86 ± 2.48, The visual pain score average of patients in the VRG group is 4.12 ± 2.16, visual anxiety scale 3.14 ± 1.76 and visual patient satisfaction scale 7.64 ± 3.26. In short, it has been detected that the pain, anxiety, and patient satisfaction score averages of patients in the VRG group are statistically significant compared to the pain score averages of patients in the control group (p < .005). Our study unearthed a statistically significant relationship at an average level of negative direction between PIC post-treatment pain, anxiety, and patient satisfaction (r = - 0.476). Utilization of VRG has been detected to reduce pain and anxiety felt during the PIC process in adult patients and increase process satisfaction. VRG, a non-pharmacological, non-invasive, inexpensive, and feasible nursing initiative, may be recommended for use in pain and anxiety control in patients undergoing PIC treatment.

YOLOv3 Based Smart Assistive Navigation System for Visually Impaired Individuals

Abstract: Nowadays, some people suffer from blindness or vision impairment, either from childhood or due to other causes. These individuals frequently face difficulty to move from source to destination precisely, resulting in difficulties in their daily routines. Currently, people are using normal walking sticks, that is not useful for all the time, especially in outdoor environments, resulting in accidents and other difficulties. This paper aims to focus on improving the quality of life for blind people or visually impaired individuals by providing a suitable solution to enhance navigation and manage their needs in indoor and outdoor environments through the use of wearable devices. One proposed solution involves the method of voice assistant technology, which allows users to easily communicate and receive clear instructions. The Raspberry Pi camera module, that allows to capture images and video streaming with the Raspberry Pi, where the Ultrasonic sensor is commonly used for distance measurement to detect obstacles. Furthermore, incorporating a Neo 6m GPS module offers precise location data and navigation guidance, aiding users in reaching their destination safely and efficiently.

Deep Learning for Abnormal Human Behavior Detection in Surveillance Videos—A Survey

Detecting abnormal human behaviors in surveillance videos is crucial for various domains, including security and public safety. Many successful detection techniques based on deep learning models have been introduced. However, the scarcity of labeled abnormal behavior data poses significant challenges for developing effective detection systems. This paper presents a comprehensive survey of deep learning techniques for detecting abnormal human behaviors in surveillance video streams. We categorize the existing techniques into three approaches: unsupervised, partially supervised, and fully supervised. Each approach is examined in terms of its underlying conceptual framework, strengths, and drawbacks. Additionally, we provide an extensive comparison of these approaches using popular datasets frequently used in the prior research, highlighting their performance across different scenarios. We summarize the advantages and disadvantages of each approach for abnormal human behavior detection. We also discuss open research issues identified through our survey, including enhancing robustness to environmental variations through diverse datasets, formulating strategies for contextual abnormal behavior detection. Finally, we outline potential directions for future development to pave the way for more effective abnormal behavior detection systems.

The Role of Machine Learning Algorithms in Shaping the Hedonic Treadmill from the Perspective of Mental Accounting

The development of subscription-based video streaming services has transformed the way humans consume entertainment by granting users the freedom to watch any film, anytime, and anywhere. The presence of such services has become the biggest disruptor to conventional television stations, DVD producers, and even cinema services. Film consumption behaviors have undergone a shift triggered by the Covid-19 pandemic. Individuals have become accustomed to enjoying films from the comfort of their homes or their familiar surroundings, rather than having to travel far or purchase and store DVD discs. The streaming service business has skyrocketed and amassed a large number of subscribers. This reality indicates a change in the consumer decision-making patterns for video streaming services. Streaming services are perceived to provide more advantages or benefits compared to cost or sacrifices. In accounting studies, this phenomenon is referred to as mental accounting. Thus far, there have been few scientific studies attempting to address the topic of mental accounting in the video streaming service industry, particularly in relation to framing strategies and the creation of hedonic treadmills built from machine learning algorithms. This research aims to explain how successful video streaming service businesses create hedonic treadmill conditions for their customers, binding them to addiction. This study employs a qualitative method with an interpretative approach to video streaming services to design a conceptual model of how video streaming service businesses' machine learning algorithms create hedonic treadmills for their customers. The research findings indicate that machine learning algorithms can play a role in creating hedonic treadmills through the creation of five relevant values: compatibility, variability, originality, personalization, and flexibility through recommendation and prediction systems.

Assessment of flight range and transmission of video information when monitoring emergency situations from an unmanned aircraft in difficult meteorological conditions

The purpose of research. Modern unmanned aerial vehicles of various types provide the ability to perform various tasks of operational information collection, monitoring the state of the environment, technological facilities and territories, updating data on these facilities, as well as for reconnaissance and monitoring their condition. The purpose of the study is to establish the maximum flight range and distance necessary for the organization of a communication channel through which the video stream and flight control commands using the KAM-16 Turbo ¾ method will be transmitted between the ground control complex and the unmanned aerial vehicle in a difficult meteorological situation.Methods are based on the basics of radio electronics, diagnostics and forecasting of the technical condition of aircraft. The methods of multicriteria analysis, parametric and structural synthesis were used. The principles of transmitting video images from drones used to monitor emergency situations were studied. A critical assessment of the maximum flight range of an unmanned aerial vehicle in a difficult meteorological situation was carried out.Results. Analytical formulas for determining the energy potential of a data transmission channel in line of sight are presented. Graphs of the dependence of the energy reserve in the data transmission channel between the unmanned aerial vehicle and the ground control system are presented, which make it possible to determine the maximum distance for transmitting video images in Full HD quality in the 2.4 GHz band. Calculations were carried out on the basis of complex analytical expressions and graphical dependences of the flight range of a micro UAV on the speed of tailwind and headwind were constructed. The maximum headwind speed is calculated, which does not allow the use of micro UAVs in blizzard conditions.Conclusion. A promising area of study of the use of UAV in emergency monitoring are micro-class copters capable of detecting victims by transmitting video information from a thermal imager during search and rescue operations in difficult weather conditions, such as snowstorms.

CA-Live360: Crowd-assisted transcoding and delivery for live 360-degree video streaming

In the burgeoning field of digital media, the popularity of live 360-degree video has witnessed an upward trajectory, underpinned by its immersive experience. In live 360-degree video streaming services, transcoding enormous video tiles and providing low-latency services for global viewers require significantly more computing and communication resources than traditional live services. Inspired by the huge crowdsourcing computational resources available at crowd, this paper designs CA-Live360, a Crowd-Assisted Live 360-degree video streaming solution, in which transcoding and delivery are supported by crowdsourcing devices. Due to the dynamic availability of resources, it is challenging to achieve low-latency live services, while guaranteeing fair transcoding. Therefore, firstly an innovative fairness-guaranteed transcoding task assignment scheme is proposed. This scheme employs a novel Fair Bandit algorithm based on the combinatorial multi-armed bandit approach to achieve low-latency transcoding while considering time-varying available resources and fairness constraints. Secondly, to reduce delivery latency, we design a transcoding-aware delivery scheme, in which a provider–requester matching algorithm is introduced to realize effective requester scheduling. Real-world trace-driven experiments demonstrate the improved performance of the proposed CA-Live360 solution in terms of system delay and fairness.

Интеллектуальная система контроля бдительности и действий машиниста

Objective: the article considers an approach to the development of a system for monitoring the vigilance and actions of the driver using technical vision and neural network models. The main purpose of the research is to improve the safety of railway transport. Methods: data collection from various sources, annotation, data purification and normalization, neural network training based on video recordings of drivers’ faces in various states and data on their behavior. Neural network learning algorithms based on the architecture of convolutional neural networks, teaching methods with a teacher, mask segmentation methods and proportional resizing of the area of interest, mask segmentation methods for determining the contour of an object in an image, deep learning algorithms such as stochastic gradient descent and error back propagation. Results: a system has been developed that determines the emotional state of the driver based on a real-time video stream, detecting signs of fatigue or distraction, warning of possible dangerous situations. This approach will allow you to quickly respond to the risks that arise in the process of train control, which allows you to increase the level of train safety. Practical significance: a system for monitoring the vigilance and actions of the driver has been developed and can be implemented on locomotives or motor-car rolling stock for real monitoring and prevention of emergency situations.

Examining the Impact of Live Streaming, Affiliate Marketing, and Flash Sale Programs on Purchase Intentions in the TikTok Shop: An Empirical Study Focused on Generation Z

Live streaming has become the latest strategy in marketing products as it provides consumers with a sense of experiencing direct shopping. In live streaming, sellers and buyers can interact directly through real-time video streaming. Marketing strategies involving third-party individuals, known as affiliate marketing, are engaged in marketing activities. Several e-commerce platforms utilize this approach as it brings benefits to all parties involved. Live streaming has become a popular promotional method, especially after the pandemic. Flash sales, with their attractive offers, are highly favored by consumers as they allow them to purchase high-quality products or services at significantly low prices, but within specific time and quantity constraints. The author is interested in understanding the most influential marketing strategies that impact consumer Purchase Intention in e-commerce, particularly on new platforms like TikTok Shop. The research method employed is descriptive statistics based on the results of statistical calculations using SEM-PLS, with primary data obtained from 96 respondents through a questionnaire.

Video Multi-Scale-Based End-to-End Rate Control in Deep Contextual Video Compression

In recent years, video data have increased in size, which results in enormous transmission pressure. Rate control plays an important role in stabilizing video stream transmissions by balancing the rate and distortion of video compression. To achieve high-quality videos through low-bandwidth transmission, video multi-scale-based end-to-end rate control is proposed. First, to reduce video data, the original video is processed using multi-scale bicubic downsampling as the input. Then, the end-to-end rate control model is implemented. By fully using the temporal coding correlation, a two-branch residual-based network and a two-branch regression-based network are designed to obtain the optimal bit rate ratio and Lagrange multiplier λ for rate control. For restoring high-resolution videos, a hybrid efficient distillation SISR network (HEDS-Net) is designed to build low-resolution and high-resolution feature dependencies, in which a multi-branch distillation network, a lightweight attention LCA block, and an upsampling network are used to transmit deep extracted frame features, enhance feature expression, and improve image detail restoration abilities, respectively. The experimental results show that the PSNR and SSIM BD rates of the proposed multi-scale-based end-to-end rate control are −1.24% and −0.50%, respectively, with 1.82% rate control accuracy.

Features and quality metrics datasets for video coding in DASH

Nowadays, the majority of media Internet traffic consists of H.264-encoded streaming videos due to its high compatibility. One of the most popular streaming technology used to deliver videos over Internet is Dynamic Adaptive Streaming over HTTP (DASH). It transmit the video as a sequence of independent short video segments tailored to the receiver’s limitations (related to several factors such as available bandwidth or resolution on reception), aiming to enhance the Quality of Experience. In this paper, we present two types of datasets created from 4065 video segments of 2 seconds. The first type consists of extracting features related to color, space, and time from the segments across different vertical resolutions (240, 360, 480, 720, 1080, 1440 and 4K). The second one includes several quality metrics obtained from the same segments when they are encoded with different compression levels in the different resolutions.

Energy consumption of data transfer: Intensity indicators versus absolute estimates

AbstractThe assessment of energy consumption of data traffic for Internet services usually relies on energy intensity figures (in Wh/GB). In this paper, we argue against using these indicators for evaluating the evolution of energy consumption of data transmission induced by changes in Internet usage. We describe a model that estimates global impacts for different scenarios of Internet usages and technological hypothesises, and show that it can overcome some limitations of intensity indicators. We experiment the model on four use‐cases: basic usage, video streaming, large downloads, and video conferencing. Results show that increasing the resolution of videos does increase the total energy consumption while misleadingly decreasing the power intensity indicator at the same time. In other words, a more efficient network does not necessarily mean less energy consumption.

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.

Joint resource and trajectory optimization for video streaming in UAV-based emergency indoor-outdoor communication

Joint resource and trajectory optimization for video streaming in UAV-based emergency indoor-outdoor communication

Queer media in the age of streaming video

This article contributes to an emerging field of research on the classification and organisation of film and television on streaming video platforms. While scholarship has begun to grapple with the complexities of the streaming video landscape, critical frameworks have yet to be established for examining issues of LGBTIQA+ inclusion in this context. This article explores questions about what queer media is in the streaming video era and how is this shaped by the information practices of streaming video services. Classification and organisation of titles is a significant factor in the discoverability of content on streaming video platforms. In the context of queer media, classification practices also impact the visibility of marginalised LGBTIQA+ identities, communities, and cultures. Beyond this, the categorisation practices of streaming video providers play a significant role in shaping and communicating cultural values about queer media. From an Australian vantage point, this article maps the contours of queer media in the streaming video environment, focusing on Subscription Video on Demand (SVOD) and Broadcast Video on Demand (BVOD) services. By investigating the categories that streaming video providers use to organise and highlight LGBTIQA+ film and television, this article identifies how queer media is defined as a cultural category through use of labels such as ‘pride’, the categories and subcategories that approach queer media as a constellation of niche interests, and an underlying emphasis on ‘good’ characters and positive LGBTIQA+ narratives.

A survey on digital device engagement, digital stress, and coping strategies among college students in the Philippines

ABSTRACT This study surveyed 384 Filipino college students on their digital device engagement, digital stress, and coping strategies. Findings revealed that students were heavily reliant on digital devices. They used digital devices for at least 15 minutes daily upon waking and before sleeping. They encountered significant digital stress, particularly from social media, online learning platforms, games, and video streaming. Facebook emerged as the most stress-inducing platform. While students felt a strong need for constant phone access, they seldom feared missing out on experiences. Students reported self-distraction and positive reframing as their coping strategies. Importantly, the study identified a positive correlation between digital device engagement and digital stress manifestation, emphasizing the adverse impact of extensive device use on stress levels among college students. The insights in this study may guide policymakers in developing policies that foster responsible and healthy technology use among youth, shaping the environment and resources available to them.

Processing and classifying IP packet data on the Internet based on machine learning

Nowadays, the continuous development of information technology, communication over the Internet is increasing rapidly, and network congestion has become an alarming issue. To develop communication network infrastructure in a large city, a country, or globally, streamlining and controlling network data flow to optimize communication processes and minimize network congestion is crucial and necessary. In this study, the authors analyze and process data according to the delay of Internet Protocol (IP) packets, using machine learning models with the Random Forest (RF) and the Support Vector Machines (SVM) method to classify IP packets. The primary goal of classifying packets by delay is to optimize network performance by prioritizing processing of low-delay packets, ensuring stable and uninterrupted online services such as video streaming and voice calls. Furthermore, it is easy to manage and control packet traffic, hence minimizing network congestion at the router. ® 2024 Journal of Science and Technology - NTTU