• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.11a
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• pp.199-202
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• 2012

인터넷 네트워크의 발달로 3D 콘텐츠와 같은 대용량의 미디어 파일을 인터넷 프로토콜을 이용하여 전송 가능해 졌다. 또한 사용자는 스마트 폰, 스마트 TV, tablet-PC 등과 같은 다양한 디바이스를 통해 인터넷 접근이 가능한 여러 장소에서 원하는 콘텐츠를 전달받아 소비하는 형태가 널리 퍼지고 있다. 이에 따라 안정적인 인터넷 네트워크 환경의 필요성과 IP기반 스트리밍 서비스와 같은 기술의 중요성이 부각되고 있는 실정이다. 하지만 유동적인 인터넷 환경에서는 스트리밍 서비스의 QoS(Quality of Service)를 보장하기 힘들고, 3D 입체영상과 같은 고화질, 고용량의 미디어 파일을 대상으로 할 경우 사용자에게 끊김없는 스트리밍 서비스를 제공하기 어렵다. 이러한 문제를 해결하기 위해서, 인터넷 네트워크 환경을 고려하여 사용자들이 원하는 콘텐츠를 고품질 혹은 저 품질로 제공 할 수 있는 적응적 스트리밍 서비스에 관한 기술 개발이 등장하게 되었고, 현재 국제 표준화 기구인 MPEG(Moving Picture experts Group)에서 DASH(Dynamic Adaptive Streaming over HTTP)라는 이름으로 표준화가 진행 중에 있다. 이에 본 논문에서는 DASH를 이용한 3D 콘텐츠의 적응적 스트리밍 서비스를 위하여 스테레오스코픽 영상의 세그먼트 파일규격 구성방안에 대해서 제안한다. DASH를 이용한 3D 스트리밍 서비스는 사용자들에게 IP 망을 통해 다양한 품질의 3D 콘텐츠를 제공할 수 있으며, 또한 하나의 3D 콘텐츠로 다양한 디바이스에 적용 가능하다는 이점이 있다.

• Journal of Broadcast Engineering
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• v.20 no.2
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• pp.283-299
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• 2015

In this paper, we propose new broadcasting services that combine MMT (MPEG Media Transport) standard with MPEG-DASH (Dynamic Adaptive Streaming over HTTP) standard. MMT is a next-generation multimedia transport standard that is IP-friendly and provides functionalities appropriate for hybrid broadcasting that uses broadcast physical channels and the Internet simultaneously. MPEG-DASH enables media streaming services that can be dynamically adaptive both to the network traffic conditions of wired and/or wireless Internet and the receiving entity environment. We explain the scenarios of the proposed broadcasting services and demonstrate that various hybrid broadcasting services can be easily realized through the combined usage of MMT and MPEG-DASH. By making the test bitstreams containing contents for the new services and developing the receiver back-end software that performs the function of the receiving entity for the new services on personal computers, we verified that the proposed scenarios can be realized.

• 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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• 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 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.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.5
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• pp.297-307
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• 2022

The simple procedural segment selection algorithm commonly used in Dynamic Adaptive Streaming over HTTP (DASH) reveals severe weakness to provide high-quality streaming services in the integrated mobile networks of various wired and wireless links. A major issue could be how to properly cope with dynamically changing underlying network conditions. The key to meet it should be to make the segment selection algorithm much more adaptive to fluctuation of network traffics. This paper presents a system architecture that replaces the existing procedural segment selection algorithm with a deep reinforcement learning algorithm based on the Asynchronous Advantage Actor-Critic (A3C). The distributed A3C-based deep learning server is designed and implemented to allow multiple clients in different network conditions to stream videos simultaneously, collect learning data quickly, and learn asynchronously, resulting in greatly improved learning speed as the number of video clients increases. The performance analysis shows that the proposed algorithm outperforms both the conventional DASH algorithm and the Deep Q-Network algorithm in terms of the user's quality of experience and the speed of deep learning.

• 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.

• 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.

• 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.