• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1249-1265
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• 2022

Video streaming has become one of the most popular applications for mobile devices. The network bandwidth required for video streaming continues to exponentially increase as video quality increases and the user base grows. Multi-Path TCP (MPTCP), which allows devices to communicate simultaneously through multiple network interfaces, is one of the solutions for providing robust and reliable streaming of such high-definition video. However, mobile video streaming over MPTCP raises new concerns, e.g., power consumption and cellular data usage, since mobile device resources are constrained, and users prefer to minimize such costs. In this work, we propose a mobile video streaming framework over MPTCP (mDASH) to reduce the costs of energy and cellular data usage while preserving feasible streaming quality. Our evaluation results show that by utilizing knowledge about video behavior, mDASH can reduce energy consumption by up to around 20%, and cellular usage by 15% points, with minimal quality degradation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.5149-5167
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• 2017

Reducing energy consumption in a wireless video sensor network (WVSN) is a crucial problem because of the high video data volume and severe energy constraints of battery-powered WVSN nodes. In this paper, we present an adaptive dynamic resizing approach for a SRAM communication buffer in a WVSN node in order to reduce the energy consumption and thereby, to maximize the lifetime of the WVSN nodes. To reduce the power consumption of the communication part, which is typically the most energy-consuming component in the WVSN nodes, the radio needs to remain turned off during the data buffer-filling period as well as idle period. As the radio ON/OFF transition incurs extra energy consumption, we need to reduce the ON/OFF transition frequency, which requires a large-sized buffer. However, a large-sized SRAM buffer results in more energy consumption because SRAM power consumption is proportional to the memory size. We can dynamically adjust any active buffer memory size by utilizing a power-gating technique to reflect the optimal control on the buffer size. This paper aims at finding the optimal buffer size, based on the trade-off between the respective energy consumption ratios of the communication buffer and the radio part, respectively. We derive a formula showing the relationship between control variables, including active buffer size and total energy consumption, to mathematically determine the optimal buffer size for any given conditions to minimize total energy consumption. Simulation results show that the overall energy reduction, using our approach, is up to 40.48% (26.96% on average) compared to the conventional wireless communication scheme. In addition, the lifetime of the WVSN node has been extended by 22.17% on average, compared to the existing approaches.

• KIISE Transactions on Computing Practices
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• v.22 no.7
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• pp.326-331
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• 2016

The proportion of video content consumption is growing dramatically but some users avoid it. The reasons are initial time to load, lack of the time to watch video content, and particularly a traffic issues on mobile devices. The proposed Video-to-Images(V2I) technology offers a new user experience to end users through converting video into images without content providers' or users' effort. Using the V2I technology, consumption methods of video content with new type of content by users and the advantages of the new user experience will be introduced. Furthermore, the overall architecture of the V2I will be explained.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.1
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• pp.157-170
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• 2012

Recently, user-created video shows high increasing in production and consumption. Among these, videos records an identical subject in limited space with multi-view are coming out. Occurring main reason of this kind of video is popularization of portable camera and mobile web environment. Multi-view has studied in visually representation technique fields for point of view. Definition of multi-view has been expanded and applied to various contents authoring lately. To make user-created videos into multi-view contents can be a kind of suggestion as a user experience for new form of video consumption. In this paper, we show the possibility to make user-created videos into multi-view video content through analyzing multi-view video contents even there exist attribute differentiations. To understanding definition and attribution of multi-view classified and analyzed existing multi-view contents. To solve time axis arranging problem occurred in multi-view processing proposed audio matching method. Audio matching method organize feature extracting and comparing. To extract features is proposed MFCC that is most universally used. Comparing is proposed n by n. We proposed multi-view video contents that can consume arranged user-created video by user selection.

• ETRI Journal
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• v.27 no.5
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• pp.504-510
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• 2005

This paper proposes a novel low-power video decoding scheme. In the encoded video bitstream, there is quite a large number of non-coded blocks. When the number of the non-coded blocks in a frame is known at the start of frame decoding, the workload of the video decoding can be estimated. Consequently, the supply voltage of very large-scale integration (VLSI) circuits can be lowered, and the power consumption can be reduced. In the proposed scheme, the encoder counts the number of non-coded blocks and stores this information in the frame header of the bitstream. Simulation results show that the proposed scheme reduces the power consumption to about 1/10 to 1/20.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.7C
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• pp.654-662
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• 2009

Video streams for mobile video streaming can be encoded to fit the available network bandwidth by controlling three factors: temporal resolution, spatial resolution, and picture quality. The controlling of picture quality by modifying the quantization parameter (QP) is most widely used. In this paper, we demonstrate that reducing the spatial resolution adaptively can be more efficient in terms of picture quality and energy consumption in low bit-rate environment, and present a model to find the optimal spatial resolution for the available bandwidth. Adaptive spatial resolution control scheme is especially effective when the bandwidth between the video server and the mobile device varies considerably with time, and when the mobile device is sensitive to energy consumption. Our scheme can improve the picture quality by approximately O.5dB and reduce energy consumption by more than 50% compared to the conventional video coding in low bit-rate environment.

• Information Systems Review
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• v.25 no.3
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• pp.121-138
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• 2023

The advancement in mobile technology brought disruptive innovation in media industry. The introduction of mobile devices broke spatial and temporal restrictions in media consumption. This study investigates the impact of mobile channel adoption on video viewing behavior, using real-world dataset obtained from a particular on-demand service provider in South Korea. We find that the adoption of a mobile channel significantly increases the total viewing time of video-on-demand via TV and the number of contents viewed. Our results suggest that the mobile channels act as a complement channel to conventional TV channels. We provide theoretical and practical insights on consumer usage in the emerging over-the-top market.

• 한국정보디스플레이학회:학술대회논문집
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• 2005.07b
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• pp.1584-1587
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• 2005

Although the picture quality of today's displays is very good already, a continuous improvement is desirable as the new larger display sizes increase the visibility of artifacts. A contributing factor for picture quality enhancement through smart video processing and algorithm design is the information gathered from video statistics. Interesting parameters gathered from video statistics are e.g. the image- and display load, the usage of the color gamut, the estimated power consumption and the occurrence of static image parts. Examples of applications that can benefit from video statistics are power calculations, color gamut mapping algorithms, dynamic backlight control for LCD panels and LED backlights for LCD panels.

• Journal of the HCI Society of Korea
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• v.2 no.1
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• pp.1-12
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• 2007

In this paper, we propose a video service system that reflects user interaction in the multimedia contents to increase user satisfaction in the restricted content consumption environment. The proposed system is based on the multiple ROI(Region of Interest) coding in H.264/AVC Scalable Video Coding (SVC). The proposed system provides scalable video quality by adopting SVC. And by bidirectional communication between user and video server, it enables user to select meaningful ROI among multiple ROIs by user interaction. To verify the usefulness of the proposed system, we demonstrate it with a test-bed on which user interactive ROIs are implemented.

• Journal of Computing Science and Engineering
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• v.5 no.4
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• pp.338-345
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• 2011

Nowadays mobile devices are used for various applications such as making voice/video calls, browsing the Internet, listening to music etc. The average battery consumption of each of these activities and the length of time a user spends on each one determines the battery lifetime of a mobile device. Previous methods have provided predictions of battery lifetime using a static battery consumption rate that does not consider user characteristics. This paper proposes an approach to predict a mobile device's available battery lifetime based on usage patterns. Because every user has a different pattern of voice calls, data communication, and video call usage, we can use such usage patterns for personalized prediction of battery lifetime. Firstly, we define one or more states that affect battery consumption. Then, we record time-series log data related to battery consumption and the use time of each state. We calculate the average battery consumption rate for each state and determine the usage pattern based on the time-series data. Finally, we predict the available battery time based on the average battery consumption rate for each state and the usage pattern. We also present the experimental trials used to validate our approach in the real world.