• Journal of KIISE
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• v.43 no.7
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• pp.827-833
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• 2016

HTTP Adaptive Streaming (HAS) is a technique that adapts its video quality to network conditions for providing Quality of Experience. In the HAS approach, a video content is encoded at multiple bitrates and the encoded video content is divided into several video segments. A HAS player estimates the network bandwidth and adjusts the video bitrate based on estimated bandwidth. However, the segment scheduler in the conventional HAS player requests video segments periodically without considering TCP. If the waiting duration for the next segment request is quite long, the TCP connection can be initialized and it restarts slow-start. Slow-start causes the reduction in TCP throughput and consequentially leads to low-quality video streaming. In this study, we propose a TCP-aware segment scheduling scheme to improve performance of HAS service. The proposed scheme adjusts request time for the next video request to prevent initialization of TCP connection and also considers the point of scheduling time. The simulation proves that our scheme improves the Quality of Service of the HAS service without buffer underflow issue.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.2
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• pp.57-62
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• 2014

In order to provide real-time multimedia streaming service, a lot of system resources and network bandwidth required. Thus each computer is any computer other equivalent has the ability to act as a client and a server Peer-to-Peer(P2P) architecture with much interest. In this paper, techniques of P2P content that requires a user to efficiently retrieve the desired time in the streaming service have placed the focus of the research techniques. In a number present in the P2P contents, the user requests to find out the desired amount of time the content streaming services in order to provide seamless lookup latency contents search algorithm to minimize the study. P2P based smart network system and the structure of the super-node and the peer node is composed of super-gateway. Smart network system architecture proposed by performing a content search algorithm. The user requests a desired content, the service can be retrieved within the provided the flexibility.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.399-404
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• 2019

This study proposes a strategy for developing Korean entertainment video service by studying the current status of user's experience of OTT(Over the Top, online video streaming service), mainly Netflix and Watcha Play. Firstly, as case study research, I investigated the features of domestic video streaming services and that of foreign services and K-content service needs. Secondly, I interviewed eight Netflix and Watcha Play users to understand the user experience and the demand for K-content video streaming service. As a result, I was able to derive two points about the strategy. First, isolated channel strategy. Second, content diversification and personalization strategy. This study is meaningful that it presented a strategy for the direction of the Korean entertainment industry. I hope that the follow-up study will help improve the Korean entertainment industry and help develop Korea's entertainment strategy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.308-324
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• 2018

HTTP adaptive streaming (HAS) is a promising technology for delivering video content over the Internet. HAS-based video streaming solutions rely on bandwidth estimation to select the appropriate video bitrate. Video streaming solutions that consider network conditions provide users with seamless video playback. However, when multiple clients compete for a common bottleneck link, conventional bandwidth estimation schemes that consider only one client overestimate the network bandwidth due to the ON-OFF traffic pattern. The bandwidth overestimation can cause Quality of Experience (QoE) degradation, such as unnecessary changes in video quality, and unfairness of video quality. In this paper, we propose a client-side bandwidth estimation scheme to obtain a better QoE of HAS in the multiple-client environment. The proposed scheme differentiates the client buffer status according to the buffer occupancy, and then estimates the available network bandwidth based on the buffer status and segment throughput. We evaluate the performance of HAS implemented in the ns-3 network simulator. Simulation results show that compared with the conventional schemes, the proposed scheme can enhance the QoE.

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

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3804-3819
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• 2018

The increasing popularity of HTTP adaptive video streaming services has dramatically increased bandwidth requirements on operator networks, which attempt to shape their traffic through Deep Packet inspection (DPI). However, Google and certain content providers have started to encrypt their video services. As a result, operators often encounter difficulties in shaping their encrypted video traffic via DPI. This highlights the need for new traffic classification methods for encrypted HTTP adaptive video streaming to enable smart traffic shaping. These new methods will have to effectively estimate the quality representation layer and playout buffer. We present a new machine learning method and show for the first time that video quality representation classification for (YouTube) encrypted HTTP adaptive streaming is possible. The crawler codes and the datasets are provided in [43,44,51]. An extensive empirical evaluation shows that our method is able to independently classify every video segment into one of the quality representation layers with 97% accuracy if the browser is Safari with a Flash Player and 77% accuracy if the browser is Chrome, Explorer, Firefox or Safari with an HTML5 player.

• The KIPS Transactions:PartA
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• v.13A no.3 s.100
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• pp.211-222
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• 2006

The cluster system with shared-nothing storage cannot escape from the problem of skewed request toward specific contents. This paper, therefore, suggests streaming method using MDC (Multiple Description Coding) instead of using single original content; this method is able to cope with skewed request in shared-nothing storage server as well as to continue to provide services in case of the system failure. Also, the system can support adaptive streaming service according to user player type, network status, the load of server, and client.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.830-832
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• 2013

The mobile phone has now evolved into an effective multimedia devices to watch video content with your PC in addition to the calling features. Thus, the effectiveness of the video content streaming services smartphone will be available. And content should be able to deliver effectively. Be provided with textbook images and video of the speaker means that the effective content delivery. In this paper, we propose a integrated video management system that can be real-time VOD services on the Internet as input Multi-Source of audio-video, video content encoding system to meet the requirements of the above two.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.276-284
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• 2022

In this paper, we implemented cloud rendering using WebRTC for high-quality AR and VR services. Cloud rendering is an applied technology of cloud computing. It efficiently handles the rendering of large volumes of 3D content. The conventional VR and AR service is a method of downloading 3D content. The download time is delayed as the 3D content capacity increases. Cloud rendering is a streaming method according to the user's point of view. Therefore, stable service is possible regardless of the 3D content capacity. In this paper, we implemented cloud rendering using WebRTC and analyzed its performance. We compared latency of 100MB, 300MB, and 500MB 3D AR content in 100Mbps and 300Mbps internet environments. As a result of the analysis, cloud rendering showed stable latency regardless of data volume. On the other hand, the conventional method showed an increase in latency as the data volume increased. The results of this paper quantitatively evaluate the stability of cloud rendering. This is expected to contribute to high-quality VR and AR services

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3635-3654
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• 2015

With the rapid development of smart devices and mobile Internet, the video application plays an increasingly important role on mobile devices. Understanding user behavior patterns is critical for optimized operation of mobile live streaming systems. On the other hand, volume based billing models on cloud services make it easier for video service providers to scale their services as well as to reduce the waste from oversized service capacities. In this paper, the watching behaviors of a commercial mobile live streaming system are studied in a content-centric manner. Our analysis captures the intrinsic correlation existing between popularity and watching intensity of programs due to the synchronized watching behaviors with program schedule. The watching pattern is further used to estimate traffic volume generated by the program, which is useful on data volume capacity reservation and billing strategy selection in cloud services. The traffic range of programs is estimated based on a naive popularity prediction. In cross validation, the traffic ranges of around 94% of programs are successfully estimated. In high popularity programs (>20000 viewers), the overestimated traffic is less than 15% of real happened traffic when using upper bound to estimate program traffic.