• International Journal of Computer Science & Network Security
• /
• v.23 no.3
• /
• pp.81-93
• /
• 2023

The popularity of short-form video platforms like TikTok has increased recently. Short-form videos are significantly shorter than traditional videos, and viewers regularly switch between different types of content to watch. Therefore, a successful prefetching strategy is essential for this novel type of video. This study provides a resource-effective prefetching technique for streaming short-form videos. The suggested solution dynamically adjusts the quantity of prefetched video data based on user viewing habits and network traffic conditions. The results of the experiments demonstrate that, in comparison to baseline approaches, our method may reduce data waste by 21% to 83%, start-up latency by 50% to 99%, and the total time of Re-buffering by 90% to 99%.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.11
• /
• pp.4585-4603
• /
• 2015

Video streaming services make up a large proportion of Internet traffic on both fixed and mobile access throughout the world. Adaptive streaming allows for dynamical adaptation of the bitrate with varying network conditions, to guarantee the best user experience. Adaptive bitrate algorithms face a significant challenge in correctly estimating the throughput as it varies widely over time. In this paper, we first evaluate the throughput estimation techniques and show that the method that we have used offers stable response to throughput fluctuations while maintaining a stable playback buffer. Then, we propose an adaptive bitrate scheme that intelligently selects the video bitrates based on the estimated throughput and buffer occupancy. We show that the proposed scheme improves viewing experience by achieving a high video rate without taking unnecessary risks and by minimizing the frequency of changes in the video quality. Furthermore, we show that it offers a stable response to short-term fluctuations and responds swiftly to large fluctuations. We evaluate our algorithm for both constant bitrate (CBR) and variable bitrate (VBR) video content by taking into account the segment sizes and show that it significantly improves the quality of video streaming.

• Journal of Advanced Information Technology and Convergence
• /
• v.10 no.2
• /
• pp.33-48
• /
• 2020

By Digital Transformation, new technologies like ML (Machine Learning), Big Data, Cloud, VR/AR are being used to video streaming technology. We choose ML to provide optimal QoE (Quality of Experience) in various network conditions. In other words, ML helps DASH in providing non-stopping video streaming. In DASH, the source video is segmented into short duration chunks of 2-10 seconds, each of which is encoded at several different bitrate levels and resolutions. We built and compared the performances of five prototypes after applying five different machine learning algorithms to DASH. The prototype consists of a dash.js, a video processing server, web servers, data sets, and five machine learning models.

• Educational Technology International
• /
• v.14 no.1
• /
• pp.1-26
• /
• 2013

This study is to identify whether three types of online instruction (text-based, video-based, and video-based instruction without text) and age category have a different influence on students' comprehension and motivation. Online students were randomly assigned to one of six groups, and they attended two-week online lectures via Course Management System. The comprehension test and the short form of IMMS were implemented when 114 participants accomplished two-week online lectures. The results revealed that using instructional video in online instruction is more effective instructional medium than text only in order to promote learner's motivation. Besides, older adults aged 41-60 are significantly different from younger adults (21-40 years old) in terms of students' comprehension. Furthermore, three types of online instructions are likely to be influenced by age category.

• Journal of KIISE:Information Networking
• /
• v.37 no.5
• /
• pp.363-373
• /
• 2010

The existing QoS mechanisms for video streaming are short of the consideration for various user environments and the characteristic of streaming applying programs. In order to overwhelm this problem, studies on the video streaming protocols exploiting scalable video coding (SVC), which provide spatial, temporal, and qualitative scalability in video coding, are progressing actively. However, these protocols also have the problem to deepen network congestion situation, and to lower fairness between other traffics, as they are not equipped with congestion control mechanisms. SVC based streaming protocols also have the problem to overlook the property of videos encoded in SVC, as the protocols transmit the streaming simply by extracting the bitstream which has the maximum bit rate within available bandwidth of a network. To solve these problems, this study suggests TCP-friendly network adaptive SVC streaming(T-NASS) protocol which considers both network status and SVC bitstream property. T-NASS protocol extracts the optimal SVC bitstream by calculating TCP-friendly transmission rate, and by perceiving the network status on the basis of packet loss rate and explicit congestion notification(ECN). Through the performance estimation using an ns-2 network simulator, this study identified T-NASS protocol extracts the optimal bitstream as it uses TCP-friendly transmission property and perceives the network status, and also identified the video image quality transmitted through T-NASS protocol is improved.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.7
• /
• pp.2513-2530
• /
• 2021

With the fast development of information and communication technologies, video streaming services and applications are increasing rapidly. However, the network condition is volatile. In order to provide users with better quality of service, it is necessary to develop an accurate and low-complexity model for Quality of Experience (QoE) prediction of time-varying video. Memory effects refer to the psychological influence factor of historical experience, which can be taken into account to improve the accuracy of QoE evaluation. In this paper, we design subjective experiments to explore the impact of Short-Term Memory (STM) on QoE. The experimental results show that the user's real-time QoE is influenced by the duration of previous viewing experience and the expectations generated by STM. Furthermore, we propose analytical models to determine the relationship between intrinsic video quality, expectation and real-time QoE. The proposed models have better performance for real-time QoE prediction when the video is transmitted in a fluctuate network. The models are capable of providing more accurate guidance for improving the quality of video streaming services.

• Journal of Industrial Convergence
• /
• v.20 no.5
• /
• pp.69-76
• /
• 2022

This paper summarizes the features and functions of game video on each platform through a study on streaming technology. Game videos have a high relationship with game content itself for the purpose of marketing and disseminating game play methods using games as the subject matter. The video length is short and it is easy to spread. The main types of game video content are game commentary, game strategy, funny, imitative, music video, and game information. As a game promotion video function, etc., it is used as a direct or indirect publicity for the game by the producer and the user for each reason. This article analyzed the production subject and production conditions of game videos, focusing on the production process. The production subject is divided into professional creators, non-professional creators, and general users. The motive for producing game videos is mainly to obtain economic benefits. The production process was extracted and presented through examples of game videos.

• International journal of advanced smart convergence
• /
• v.10 no.4
• /
• pp.273-277
• /
• 2021

As society gradually enters a virtual, non-face-to-face society, the use of online content is increasing as well. In particular, as smartphones are thoroughly established in our daily life, the platforms of webtoons, mobile broadcasting, and education are shifting from personal computers to smartphones. Recently, the development of the Over-The-Top media service (OTT service) enabled streaming services of various media contents through the internet and activation of IPTV. Therefore, the rapid increase of popularity of short-form content is a natural phenomenon with smartphone platforms with fast, improvised, and endless communication. Lately, TikTok became the favored platform with prosumers, defined as people who are both producers and consumers. In this study, I studied the experiential response of YouTube and TikTok users as representative examples of a short-form content platform developed after the 2000s, the flourishing years of digital content with a length of 30 seconds.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.13 no.8
• /
• pp.1571-1579
• /
• 2009

This paper proposes a P2P(Peer-to-Peer) streaming protocol which allows various QoS(Qualify of Service) requirements of video streaming to be supported for various personal IPTV applications in ubiquitous Internet environments. The proposed P2P streaming protocol takes fairly short startup delay, guarantees end-to-end delay bound and bandwidth requirements, and supports reliability level of video streaming for an IPTV application. The QoS-based P2P streaming protocol can properly use not only the advantages of scalability, availability, and ubiquity of P2P streaming but also complement its disadvantages of performance and reliability so that various types of personal IPTV applications can be properly implemented in the existing broadband Internet environments.

• The Journal of the Korea Contents Association
• /
• v.18 no.3
• /
• pp.34-40
• /
• 2018

In this study, the optimal buffer size is calculated for seamless video playback on a mobile device. Buffer means the memory space for multimedia packet which arrives in mobile device for video play such as VOD service. If the buffer size is too large, latency time before video playback can be longer. However, if it is too short, playback service can be paused because of shortage of packets arrived. Hence, the optimal buffer size insures QoS of video playback on mobile devices. We model the process of buffering into a discret-time queueing model. Mean busy period length and mean waiting time of Geo/G/1 queue with N-policy is analyzed. After then, we uses the main performance measures to present numerical examples to decide the optimal buffer size on mobile devices. Our results enhance the user satisfaction by insuring the seamless playback and minimizing the initial delay time in VOD streaming process.