• 대한임베디드공학회논문지
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• 제10권6호
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• pp.353-362
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• 2015

In order to provide smart devices with high quality multimedia streaming services, an adaptive streaming technique over HTTP has been received much attention recently and the Dynamic Adaptive Streaming over HTTP (DASH) standard has been established. In DASH, however, the technique to select an appropriate quality of multimedia based on the performance metrics measured in a smart device might have some difficulties to reflect the capabilities of other neighboring smart devices and dynamic network conditions in real time. To solve the problem, this paper proposes a novel software agent approach, called DASH agent (DA), which gathers and analyzes the device capabilities and dynamic network conditions in real time and finally determines the highest achievable quality of segment to meet the best Quality of Experience (QoE) in current situations. The simulation results show that our approach provides higher quality of multimedia segments with less frequency of quality changes to lower quality of multimedia segments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권9호
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• pp.4572-4586
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• 2019

Mobile Edge Computing (MEC) and Information-Centric Networking (ICN) are essential network architectures for the future Internet. The advantages of MEC and ICN such as computation and storage capabilities at the edge of the network, in-network caching and named-data communication paradigm can greatly improve the quality of video streaming applications. However, the packet loss in wireless network environments still affects the video streaming performance and the existing loss recovery approaches in ICN does not exploit the capabilities of MEC. This paper proposes a Deep Learning based Loss Recovery Mechanism (DL-LRM) for video streaming over MEC based ICN. Different with existing approaches, the Forward Error Correction (FEC) packets are generated at the edge of the network, which dramatically reduces the workload of core network and backhaul. By monitoring network states, our proposed DL-LRM controls the FEC request rate by deep reinforcement learning algorithm. Considering the characteristics of video streaming and MEC, in this paper we develop content caching detection and fast retransmission algorithm to effectively utilize resources of MEC. Experimental results demonstrate that the DL-LRM is able to adaptively adjust and control the FEC request rate and achieve better video quality than the existing approaches.

• Asia pacific journal of information systems
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• 제34권2호
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• pp.493-517
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• 2024

E-commerce has grown to be perceived as an integral component of modern customers' lives. Fast innovation enables businesses to implement new business ideas that enhance customers' shopping experiences. The motive is to study the allurement of unrestrained buying behaviors resulting from Live Streaming Commerce in the presence of Emotional imagery. The conceptual model and hypotheses for the study have been framed based on the SOR model. A survey was conducted in north India, where data was collected from 577 consumers experiencing live streaming and analyzed with the help of AMOS and SPSS software. The repulsive behavior scale has been developed by using qualitative research. The findings revealed that there is a significant relationship between the stimulus of livestreaming and unrestrained buying behaviors with the mediating role of emotional imagination. Two crucial mediating factors, pleasure, and arousal (fervent imagination), have successfully predicted experiential shopping behavior. The study has implications for online marketers and policymakers, as marketers can use our developed model to understand consumers' different buying behaviors, and policymakers can select and design specific features for the social presence of live streaming. Integrating three different types of unrestrained buying behavior influenced by live streaming would add to the literature. The study adds value to the literature by developing a scale to measure repulsive behavior after testing and validating with experts.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2005년도 가을 학술발표논문집 Vol.32 No.2 (1)
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• pp.280-282
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• 2005

In this paper we propose several mapping schemes for streaming video generated by state-of-the-art H.264 codec over IEEE 802.11e enabled wireless LANs. The schemes take advantage of both 802.11e's QoS mechanism and some novel features of the H.264 codec, so as to protect the most important information in terms of visual quality and reduce distortion under network congestion. The proposed methods are evaluated by means of the H.264 reference software codec, network simulation, and objective video quality measurements. Results show that the proposed methods achieve a robust and error resilient H.264 video streaming over wireless LANs than traditional best-effort streaming.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제5권8호
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• pp.361-368
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• 2016

고차원데이터에 대한 차원 감소 기법들은 많이 연구되어져 온 반면, 개념 변동을 가진 고차원 스트리밍 데이터에서 적용할 수 있는 차원 감소 기법에 대한 연구는 제한적이다. 이 논문에서는 스트리밍 데이터에서 적용할 수 있는 점층적 차원 감소 기법들을 살펴보고, 개념 변동 고차원 스트리밍 데이터에 대해 분류 성능을 향상시킬 수 있도록 차원 감소를 효과적으로 적용하는 방법을 제안한다.

• 한국멀티미디어학회논문지
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• 제21권2호
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• pp.188-198
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• 2018

With recent development of high-speed wide-area wireless networks and wide spread of highperformance wireless devices, the demand on seamless video streaming services in Long Term Evolution (LTE) network environments is ever increasing. To meet the demand and provide enhanced Quality of Experience (QoE) with mobile users, the Dynamic Adaptive Streaming over HTTP (DASH) has been actively studied to achieve QoE enhanced video streaming service in dynamic network environments. However, the existing DASH algorithm to select the quality of requesting video segments is based on a procedural algorithm so that it reveals a limitation to adapt its performance to dynamic network situations. To overcome this limitation this paper proposes a novel quality selection mechanism based on a Deep Q-Network (DQN) model, the DQN-based DASH ABR($DQN_{ABR}$) mechanism. The $DQN_{ABR}$ mechanism replaces the existing DASH ABR algorithm with an intelligent deep learning model which optimizes service quality to mobile users through reinforcement learning. Compared to the existing approaches, the experimental analysis shows that the proposed solution outperforms in terms of adapting to dynamic wireless network situations and improving QoE experience of end users.

• 전기전자학회논문지
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• 제24권1호
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• pp.46-51
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• 2020

모바일 기기에서 Wi-Fi와 Bluetooth 연결이 일상화 되면서, Wi-Fi, Bluetooth 등의 두 개 이상의 이기종 무선 라디오를 하나의 칩에 통합한 Wi-Fi 및 Bluetooth 콤보 모듈이 보편화 되었다. 콤보칩 기법의 핵심 요건은 통합칩 사용으로도 사용자 경험(QoE)의 품질을 저하되는 등의 성능저하가 있어서는 안되며, 따라서 다양한 환경에서 이 요건을 보장하기 위해서는 콤보모듈의 행동을 특성화하고 이해하는 것이 매우 중요하다. 본 연구에서는 Wi-Fi / Bluetooth 콤보 통신 모듈을 장착한 모바일 기기에서 모바일 스트리밍을 이용할 경우, Bluetooth 사용이 사용자 체감 품질(Qualty-of-Experience)에 미치는 영향을 조사한다. 실측을 통한 실험 결과, Wi-Fi와 블루투스를 동시에 사용하는 환경에서는 Wi-Fi만을 이용하는 환경에 비해 최대 55%의 성능 저하를 보인 것으로 나타났다. 본 연구 이기종 통신 모듈의 사용에 따른 물리 및 링크 계층의 전송 스케쥴링이 최상위 사용자 계층의 성능에 미치는 영향을 밝혀냈다는 중요성을 갖는다.

• 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.

• 한국통신학회논문지
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• 제29권6A호
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• pp.690-696
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• 2004

국내 지상파 데이터 방송 전송 규격인 ATSC(advanced television systems committee)-A/9O에서는 메인 프로그램과 긴밀한 시간관계가 요구되는 동기화 데이터로써 DSM-CC(Digital storage media command and control) section의 형태로 전송되는 비스트리밍 동기화 데이터와 PES(packetized elementary stream) packet의 형태로 전송되는 스트리밍 동기화 데이터를 규정하고 있다. 본 논문의 목적은 ATSC A/90 규격에 정의된 스트리밍 동기화 데이터 처리를 위한 알고리즘을 구현하고 검증하는데 있다. 이를 위하여 windows 환경 하에서 스트리밍 동기화 데이터를 포함한 PES packet을 분석하는 parser와 분석된 데이터의 재생을 위한 player를 구현하였다. 또한, 구현된 알고리즘의 검증을 위하여 디지털 데이터 방송 수신카드를 탑재한 한국전자통신연구원의 PC형 STB(Set-top Box)에 알고리즘을 포팅하여 실험 환경을 구축하고, 스트리밍 동기화 데이터를 삽입한 MPEG-2 TS(transport stream)를 이용하여 알고리즘이 포팅된 시스템의 동작을 확인하였다.

• 한국컴퓨터정보학회논문지
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• 제25권9호
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• pp.91-100
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• 2020

본 논문은 네트워크 품질에 대한 서비스의 품질과 성능을 평가할 수 있는 분산형/탈중앙 스토리지에 오디오와 비디오를 포함하는 멀티미디어 스트리밍 서비스의 프로토타입을 설계하고 구현한 통합된 평가 프레임워크를 제안한다. 본 논문의 평가 프레임워크는 기존 클라이언트·서버 구조의 확장성과 단일 장애 지점의 문제를 극복할 수 있도록 멀티미디어 스트리밍 서비스에 분산형 스토리지를 적용하였으며, 서비스 품질을 측정하고 향상 시키는 중요한 지표에 초점을 맞추었다. 특히 탈중앙 스토리지 기반의 멀티미디어 콘텐츠 스트리밍 서비스에서 스트리밍의 품질과 성능을 평가하기 위한 성능 지표를 측정할 수 있을 뿐만 아니라 네트워크 가상화 기술을 이용하여 네트워크 품질을 조정할 수 있다. 실험 결과에 따르면 제안된 프레임워크는 단일 호스트 컴퓨터에 탈중앙 스토리지 기반 스트리밍 서비스를 구축하고 운영하는 데 있어 오버헤드가 낮음을 보여주었으며, 평가 가능한 시스템 규모의 확장성을 검증하였다.