• ETRI Journal
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• 제35권1호
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• pp.27-34
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• 2013

This paper proposes a network-adaptive mechanism for HTTP-based video streaming over wireless/mobile networks. To provide adaptive video streaming over wireless/mobile networks, the proposed mechanism consists of a throughput estimation scheme in the time-variant wireless network environment and a video rate selection algorithm used to increase the streaming quality. The adaptive video streaming system with proposed modules is implemented using an open source multimedia framework and is validated over emulated wireless/mobile networks. The emulator helps to model and emulate network conditions based on data collected from actual experiments. The experiment results show that the proposed mechanism provides higher video quality than the existing system provides and a rate of video streaming almost void of freezing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권6호
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• pp.2144-2159
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• 2015

CISCO VNI predicted an average annual growth rate of 66% for mobile video traffic between 2014 and 2019 and accordingly much academic research related to video streaming has been initiated. In video streaming, Adaptive Bitrate (ABR) is a streaming technique in which a source video is stored on a server at variable encoding rates and each streaming user requests the most appropriate video encoding rate considering their channel capacity. However, these days, ABR related studies are only focusing on real-time rate adaptation omitting energy efficiency though it is one of the most important requirement for mobile devices, which may cause dissatisfaction for streaming users. In this paper, we propose an energy efficient prefetching based dynamic adaptive streaming technique by considering the limited characteristics of the batteries used in mobile devices, in order to reduce the energy waste and provide a similar level of service in terms of the average video rate compared to the latest ABR streaming technique which does not consider the energy consumption. The simulation results is showing that our proposed scheme saves 65~68% of energy at the average global mobile download speed compared to the latest high performance ABR algorithm while providing similar rate adaptation performance.

• International Journal of Internet, Broadcasting and Communication
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• 제12권4호
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• pp.166-172
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• 2020

Video streaming application such as YouTube is one of the most popular mobile applications. To adjust the quality of video for available network bandwidth, a streaming server provides multiple representations of video of which bit rate has different bandwidth requirements. A streaming client utilizes an adaptive bit rate scheme to select a proper video representation that the network can support. The download behavior of video streaming client player is governed by several parameters such as maximum buffer size. Especially, the size of the maximum playback buffer in the client player can greatly affect the user experience. To tackle this problem, in this paper, we propose the maximum buffer size optimization according to available network bandwidth and buffer status. Our simulation study shows that our proposed buffer size optimization scheme successfully mitigates playback stalls while preserving the similar quality of streaming video compared to existing ABR schemes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권8호
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권11호
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• pp.4519-4533
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• 2015

Hypertext Transfer Protocol (HTTP) adaptive streaming has become a new trend in video delivery. An HTTP adaptive streaming client needs to effectively estimate resource availability and demand. However, due to the bitrate of the video encoded in variable bitrate (VBR) mode, a bitrate mismatch problem occurs. With the rising demand for mobile devices, the likelihood of cases where two or more HTTP adaptive streaming clients share the same network bottleneck and competing for available bandwidth will increase. These mismatch and competition issues lead to network congestion, which adversely affects the Quality of Experience (QoE). To solve these problem, we propose a video rate adaptation scheme for the HTTP video streaming to guarantee and optimize the QoE. The proposed scheme estimates the available bandwidth according to the bitrate of each segment and also schedules the segment request time to expedite the response to the bandwidth variation. We used a multi-client simulation to prove that our scheme can effectively cope with drastic changes in the connection throughput and video bitrate.

• 한국멀티미디어학회논문지
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• 제21권10호
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• pp.1182-1194
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• 2018

The Dynamic Adaptive Streaming over HTTP(DASH) is envisioned to evolve to meet an increasing demand on providing seamless video streaming services in the near future. The DASH performance heavily depends on the client's adaptive quality selection algorithm that is not included in the standard. The existing conventional algorithms are basically based on a procedural algorithm that is not easy to capture and reflect all variations of dynamic network and traffic conditions in a variety of network environments. To solve this problem, this paper proposes a novel quality selection mechanism based on the Deep Q-Network(DQN) model, the DQN-based DASH Adaptive Bitrate(ABR) mechanism. The proposed mechanism adopts a new reward calculation method based on five major performance metrics to reflect the current conditions of networks and devices in real time. In addition, the size of the consecutive video segment to be downloaded is also considered as a major learning metric to reflect a variety of video encodings. Experimental results show that the proposed mechanism quickly selects a suitable video quality even in high error rate environments, significantly reducing frequency of quality changes compared to the existing algorithm and simultaneously improving average video quality during video playback.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권11호
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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.

• 정보과학회 논문지
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• 제45권2호
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• pp.187-194
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• 2018

최근 HTTP 적응적 스트리밍(HTTP adaptive streaming) 서비스가 주목을 받고 있다. 기존 HTTP 적응적 스트리밍 서비스의 비디오 품질 조절 기법은 네트워크의 대역폭이나 클라이언트의 버퍼량에 따라 비디오 품질을 조절한다. 그러나 기존 품질 조절 기법은 빈번한 대역폭 변화나 고정된 버퍼 임계로 인해 불필요한 품질 변경이 발생하여 QoE(Quality of Experience)가 저하되는 문제가 있다. 본 논문에서는 변화하는 네트워크 환경에서 HTTP 적응적 스트리밍 서비스의 QoE 향상을 위해 평균 비디오 품질을 높이면서 불필요한 품질 변경을 최소화하는 비디오 품질 유지 기법을 제안한다. 제안하는 기법은 버퍼 점유율과 비디오 품질이 높을 경우 품질 유지 기간을 길게 설정하여 높은 품질을 오래 유지한다. 실험을 통해 제안하는 기법이 평균 비디오 품질을 향상시키고 품질 변경을 최소화하여 QoE를 향상시키는 것을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권11호
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• pp.4068-4086
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• 2014

Due to the prevalence of powerful mobile terminals and the rapid advancements in wireless communication technologies, the wireless video streaming service has become increasingly more popular. Recent studies show that video streaming services via Transmission Control Protocol (TCP) are becoming more practical. TCP has more advantages than User Diagram Protocol (UDP), including firewall traversal, bandwidth fairness, and reliability. However, each video service shares an equal portion of the limited bandwidth because of the fair sharing characteristics inherent in TCP and this bandwidth fair sharing cannot always guarantee the video quality for each user. To solve this challenging problem, an Adaptive Multiple TCP (AM-TCP) scheme is proposed in this paper to guarantee the video quality for mobile devices in wireless networks. AM-TCP adaptively controls the number of TCP connections according to the video Rate Distortion (RD) characteristics of each stream and network status. The proposed scheme can minimize the total distortion of all participating video streams and maximize the service quality by guaranteeing the quality of each video streaming session. The simulation results show that the proposed scheme can significantly improve the quality of video streaming in wireless networks.

• 방송공학회논문지
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• 제23권1호
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• pp.104-114
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• 2018

최근 HTTP 기반 비디오 스트리밍 트래픽이 계속해서 증가함에 따라 HTTP 기반 적응적 스트리밍(HTTP-based Adaptive Streaming : HAS) 기술 중 하나인 DASH(Dynamic Adaptive Streaming over HTTP)가 주목받고 있다. 이에 따라 DASH 환경에서 클라이언트에게 높은 QoE(Quality of Experience)를 제공하기 위한 많은 비디오 품질 조절 기법들이 제안되어왔다. 본 논문에서는 뉴로 퍼지 시스템의 구조 중 하나인 ANFIS(Adaptive Network based Fuzzy Inference System)를 이용한 새로운 품질 조절 기법을 제안한다. 제안하는 기법은 ANFIS를 이용하여 클라이언트에게 적절한 세그먼트 비트율을 선택하는 퍼지 파라미터를 찾고, VBR(Variable Bit-Rate) 비디오의 특성을 고려하여 실제 세그먼트의 크기를 이용해 다음 세그먼트 다운로드 시간을 보다 정확하게 예측한다. 그리고 이를 이용해 시변 네트워크에서 적절하게 비디오 품질을 조절한다. NS-3를 이용한 모의실험에서 제안된 기법이 기존 기법들에 비해 높은 평균 세그먼트 비트율과 낮은 비트율 변화 횟수를 보여 클라이언트에게 향상된 QoE를 제공함을 보인다.