• The Transactions of the Korean Institute of Electrical Engineers P
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• v.65 no.4
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• pp.326-334
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• 2016

In this paper for developing and implementing the realtime HD level video streaming transmitter which is operated on the multi-platform in all network and client environment compared to the exist video live streaming transmitter. We design the realtime HD level video streaming transmitter supporting the multi-platform using the TMS320DM386 video processor of T.I company and then porting the Linux kernel 2.6.29 and implementing the RTSP(Real Time Streaming Protocol)/RTP(Real Time Transport Protocol), HLS(Http Live Streaming), RTMP(Real Time Messaging Protocol) that can support the multi-platform of video stream protocol of the received equipments (smart phone, tablet PC, notebook etc.). For proving the performance of developed video streaming transmitter, we make the testing environment for testing the performance of streaming transmitter using the notebook, iPad, android Phone, and then analysis the received video in the client displayer. In this paper, we suggest the developed the Realtime HD(High Definition) level Video Streaming transmitter performance data values higher than the exist products.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.6
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• pp.1505-1511
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• 2016

Live streaming is a method to provide media service by sending recoded media to a user's video player. In order to provide video and audio contents in real-time for a large number of users simultaneously, live streaming compatible protocols such as RTMP (Real Time Messaging Protocol), HLS (Http Live Streaming), are required. In this paper, we analyzed vulnerability of paid live streaming services with the captured packets from the applications used by six major OTT (over-the-top) companies in Korea supporting live streaming services. We found that streaming channels were not encrypted and access control mechanisms were not properly used. Thus, guest users can freely use paid live streaming services.

• ETRI Journal
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• v.37 no.4
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• pp.696-706
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• 2015

This paper presents an achievable secure videoconferencing system based on quantum key encryption in which key management can be directly applied and embedded in a server/client videoconferencing model using, for example, OpenMeeting. A secure key management methodology is proposed to ensure both a trusted quantum network and a secure videoconferencing system. The proposed methodology presents architecture on how to share secret keys between key management servers and distant parties in a secure domain without transmitting any secrets over insecure channels. The advantages of the proposed secure key management methodology overcome the limitations of quantum point-to-point key sharing by simultaneously distributing keys to multiple users; thus, it makes quantum cryptography a more practical and secure solution. The time required for the encryption and decryption may cause a few seconds delay in video transmission, but this proposed method protects against adversary attacks.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.45-52
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• 2014

The Ubiquitous-City (U-City) is a smart or intelligent city to satisfy human beings' desire to enjoy IT services with any device, anytime, anywhere. It is a future city model based on Internet of everything or things (IoE or IoT). It includes a lot of video cameras which are networked together. The networked video cameras support a lot of U-City services as one of the main input data together with sensors. They generate huge amount of video information, real big data for the U-City all the time. It is usually required that the U-City manipulates the big data in real-time. And it is not easy at all. Also, many times, it is required that the accumulated video data are analyzed to detect an event or find a figure among them. It requires a lot of computational power and usually takes a lot of time. Currently we can find researches which try to reduce the processing time of the big video data. Cloud computing can be a good solution to address this matter. There are many cloud computing methodologies which can be used to address the matter. MapReduce is an interesting and attractive methodology for it. It has many advantages and is getting popularity in many areas. Video cameras evolve day by day so that the resolution improves sharply. It leads to the exponential growth of the produced data by the networked video cameras. We are coping with real big data when we have to deal with video image data which are produced by the good quality video cameras. A video surveillance system was not useful until we find the cloud computing. But it is now being widely spread in U-Cities since we find some useful methodologies. Video data are unstructured data thus it is not easy to find a good research result of analyzing the data with MapReduce. This paper presents an analyzing system for the video surveillance system, which is a cloud-computing based video data management system. It is easy to deploy, flexible and reliable. It consists of the video manager, the video monitors, the storage for the video images, the storage client and streaming IN component. The "video monitor" for the video images consists of "video translater" and "protocol manager". The "storage" contains MapReduce analyzer. All components were designed according to the functional requirement of video surveillance system. The "streaming IN" component receives the video data from the networked video cameras and delivers them to the "storage client". It also manages the bottleneck of the network to smooth the data stream. The "storage client" receives the video data from the "streaming IN" component and stores them to the storage. It also helps other components to access the storage. The "video monitor" component transfers the video data by smoothly streaming and manages the protocol. The "video translator" sub-component enables users to manage the resolution, the codec and the frame rate of the video image. The "protocol" sub-component manages the Real Time Streaming Protocol (RTSP) and Real Time Messaging Protocol (RTMP). We use Hadoop Distributed File System(HDFS) for the storage of cloud computing. Hadoop stores the data in HDFS and provides the platform that can process data with simple MapReduce programming model. We suggest our own methodology to analyze the video images using MapReduce in this paper. That is, the workflow of video analysis is presented and detailed explanation is given in this paper. The performance evaluation was experiment and we found that our proposed system worked well. The performance evaluation results are presented in this paper with analysis. With our cluster system, we used compressed $1920{\times}1080(FHD)$ resolution video data, H.264 codec and HDFS as video storage. We measured the processing time according to the number of frame per mapper. Tracing the optimal splitting size of input data and the processing time according to the number of node, we found the linearity of the system performance.