• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.11a
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• pp.111-113
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• 2010

본 논문에서는 SVC(Scalable Video Coding) 및 MVC(Multiview Video Coding) 등의 다계층 비디오의 효율적인 적응 HTTP 스트리밍을 위한 MPEG-2 TS(Transport Stream) 헤더의 확장을 제안한다. 먼저 TS로 다중화한 SVC/MVC를 HTTP를 통하여 스트리밍 할 경우 계층별 적응 스트리밍을 지원하기 위한 기존 TS의 한계점을 분석하고, TS 헤더의 확장을 통하여 TS 레벨에서 효율적인 적응을 제공하는 시그널링 기법을 제시한다. 본 논문의 제안 기법은 TS 헤더의 private_data를 추가적으로 정의하여 스케일러빌리티 및 뷰 정보를 기술함으로써 TS 단위로 스케일러블 계층 및 뷰 간 적응 스트리밍을 제공한다.

• Journal of Broadcast Engineering
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• v.23 no.4
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• pp.549-558
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• 2018

We designed and implemented streaming server based on MEPG DASH, which is able to provide high quality video with low-latency live streaming service like 360 VR video on the existing cable network via low-spec media service devices such as IPTV and OTT(Over the Top) SettopBox. We also designed and applied management process which is cable of supporting services by cashing streaming video file(MPD, Segment Files) to reduce the server response delay time. Further more, we confimred that it is also able to provide high quality of tiled video streaming with over 50,000kbps bitrate and 8K@60P through the experiment.

• Journal of Korea Multimedia Society
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• v.20 no.1
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• pp.1-9
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• 2017

Explosive deployment of smart mobile devices such as smart phones, and tablets along with expansion of wireless internet bandwidth have enabled the deployment of mobile video streaming such that video traffic becomes the most important service in wireless networks. Recently, for more efficient video streaming services, the ISO MPEG group standardized a protocol called DASH (Dynamic Adaptive Streaming over HTTP) and the standard has been quickly adopted by many service providers such as YouTube and Netflix. Despite of the convenience of mobile streaming services, users also suffer from low QoE(Quality of Experience) due to dynamic channel fluctuations and unnecessary downloading due to high churning rates. This paper proposes a noble efficient video rate selection algorithm considering user buffer level, channel condition and churning rate. Computer simulation based performance study showed that the proposed algorithm improved the QoE significantly compared to a method that determines the video rate based on current channel conditions. Especially, the proposed method reduced the rebuffering rate, one of the most important performance factors of the QoE, to a nonnegligible level.

• Journal of KIISE
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• v.43 no.5
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• pp.606-612
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• 2016

The multi-view video service based on the DASH(Dynamic Adaptive Streaming over HTTP) switches the viewpoint or object which is selected by the user among the multiple video streams captured by multiple cameras. However, the problem is that the conventional DASH-based multi-view video service takes a long time to switch the viewpoint. The reason is that the conventional scheme switches to the new video stream after consuming all buffered segments of the previous video stream. In this paper, we propose a video streaming scheme for minimizing the viewpoint switching delay in the DASH-based multi-view video service. In order to minimize the viewpoint switching delay, the proposed scheme configures the video streams by controlling the GoP (Group of Pictures) size and controls the client buffer based on bandwidth estimation and playback buffer occupancy. Through the experimental results, we prove that the proposed scheme reduces the viewpoint switching delay.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.527-530
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• 2014

DASH is a well-known streaming technology, which was proposed in 2010 by MPEG and standardized in 2011. Major multimedia contents service providers, including Apple, Microsoft, and Adobe are all using this technology to support their media streaming services. Whenever a new service is requested to the server, the DASH technology helps servicing the multimedia streaming to client by recognizing the capacity of network and by adapting the quality of the multimedia contents. In DASH, the quality of multimedia contents will be automatically lowered to meet the fluctuating network status, when undesirable breaks interrupt the network. In this paper, we classified and analysed the advantages and disadvantages of DASH researches in three aspects: bit-rate measurement method, bandwidth aggregation method; rate adaptation metrics, algorithms and logics; user's experiences and QoE.

• Journal of Information Processing Systems
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• v.18 no.6
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• pp.729-740
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• 2022

Netflix, Amazon Prime, and YouTube are the most popular and fastest-growing streaming services globally. It is a matter of great interest for the streaming service providers to preview their service infrastructure and streaming strategy in order to provide new streaming services. Hence, the first part of the paper presents a detailed survey of the Content Distribution Network (CDN) and cloud infrastructure of these service providers. To understand the streaming strategy of these service providers, the second part of the paper deduces a common quality-of-service (QoS) model based on rebuffering time, bitrate, progressive download ratio, and standard deviation of the On-Off cycle. This model is then used to analyze and compare the streaming behaviors of these services. This study concluded that the streaming behaviors of all these services are similar as they all use Dynamic Adaptive Streaming over HTTP (DASH) on top of TCP. However, the amount of data that they download in the buffering state and steady-state vary, resulting in different progressive download ratios, rebuffering levels, and bitrates. The characteristics of their On-Off cycle are also different resulting in different QoS. Hence a thorough adaptive bit rate (ABR) analysis is presented in this paper. The streaming behaviors of these services are tested on different access network bandwidths, ranging from 75 kbps to 30 Mbps. The survey results indicate that Netflix QoS and streaming behavior are significantly consistent followed by Amazon Prime and YouTube. Our approach can be used to compare and contrast the streaming services' strategies and finetune their ABR and flow control mechanisms.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2017.11a
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• pp.46-48
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• 2017

학술대회는 동 시간대에 각기 다른 주제의 여러 세션이 운영되기 때문에 시간적으로 선택의 제약이 존재한다. 따라서 일반적으로 학회 참가자는 선택적으로 세션을 청취해야한다. 본 논문에서는 이러한 제약을 해결할 수 있는 학술대회 중계용 MPEG-DASH 기반의 HTTP 적응 스트리밍 서비스를 구현하였고, 그 결과를 보여준다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.06a
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• pp.317-319
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• 2018

최근 인터넷을 통한 UHD (Ultra High Definition) 스트리밍 서비스의 수요가 증가했으며 네트워크에 효율적으로 비디오 스트리밍 서비스를 제공하기 위해 HTTP 적응적 스트리밍 (HTTP Adaptive Streaming, HAS) 서비스가 등장하였다. 그러나 HTTP 적응적 스트리밍은 세그먼트의 ON-OFF 패턴으로 인해 다중 클라이언트 환경에서 공정성 (Fairness), 안정성 (Stability), 효율성 (Efficiency)을 저하시키는 문제가 있다. 본 논문에서는 다수의 HAS 클라이언트 환경에서 공정성, 안정성, 효율성을 향상시키기 위한 홈 공유기에서 인코딩 비트율 기반의 대역폭 할당 기법을 제안한다. 제안 기법은 OFF 구간을 줄이기 위해 인코딩 비트율에 맞추어 할당할 대역폭을 결정함으로써 안정적인 스트리밍을 보장한다. 실험을 통해 다중 HAS 클라이언트 환경에서 공정성, 안정성 및 효율성이 향상된 것을 확인하였다.

• 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.

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