• Journal of KIISE:Computer Systems and Theory
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• v.37 no.4
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• pp.195-204
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• 2010

Due to improved wireless communication technologies, it is possible to provide multimedia streaming service for mobile device clients like PDAs and cellphones. Wireless networks are serviced on low bandwidth channels and mobile devices work on limited hardware specifications. In these conditions, transcoding technologies are needed to adapt the media for streaming services to given mobile environments. To transcode from the source media to the target media for corresponding grades, transcoding servers perform transcoding jobs as exhausting their resources. Since various transcoding loads occur according to the target transconding grades, an effective transcoding load balancing policy is required among transcoding servers. In addition to transcoding process, servers should maintain QoS streams for mobile clients for total serviced times. It requires real-time requirements to support QoS for various mobile clients. In this paper, a new transcoding load distribution method is proposed. The proposed method can be driven for fair load balance between distributed transcoding servers. Based on estimated transcoding time, movie information and target transcoding bit-rate, it provides fair transcoding load distribution and also performs admission control to support QoS streams for mobile clients.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.9_10
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• pp.466-475
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• 2008

Owing to the improved wireless communication technologies, it is possible to provide streaming service of multimedia with PDAs and mobile phones in addition to desktop PCs. Since mobile client devices have low computing power and low network bandwidth due to wireless network, the transcoding technology to adapt media for mobile client devices considering their characteristics is necessary. Transcoding servers transcode the source media to the target media within corresponding grades and provide QoS in real-time. In particular, an effective load balancing policy for transcoding servers is inevitable to support QoS for large scale mobile users. In this paper, the transcoding load estimation algorithm is proposed for load balance on the distributed transcoding environments. The proposed algorithm estimates transcoding time from transcoding server information, movie information and target transcoding bit-rate. The estimated transcoding time is proved based on experiments.

• The Journal of the Acoustical Society of Korea
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• v.21 no.6
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• pp.531-542
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• 2002

Novel tandemless transcoding method for AMR and EVRC speech coders is proposed in this paper. In contrast to conventional tandem method, the parameters which is used commonly in speech coder where CELP algorithm is adapted are directly transcoded. The proposed algorithm is composed of LSP transcoding, pitch delay transcoding, gains transcoding and fixed codebook vector transcoding Evaluation results show that the novel algorithm achieves better speech quality than tandem method and reduce computational complexity and delay.

• The KIPS Transactions:PartA
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• v.16A no.3
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• pp.181-188
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• 2009

Transcoding is one of core techniques that implement VoD services according to QoS. But it consumes a lot of CPU resource. A transcoding proxy transcodes multimedia objects to meet requirements of various mobile devices and caches them to reuse later. In this paper, we propose a service policy that reduces the load of transcoding multimedia objects by degrading QoS in a transcoding proxy. Due to the tradeoff between QoS and the load of a proxy system, a transcoding proxy provides lower QoS than a client's requirement so that it can accomodate more clients.

• Journal of information and communication convergence engineering
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• v.5 no.4
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• pp.339-345
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• 2007

Video transcoding is a technique used to convert a compressed input video stream with an arbitrary format, size, and bitrate into a different attribute video stream different attributes to provide a efficient video streaming service for the customers is dispersed in the heterogeneous networks. Specifically, frames deletion occur in a transcoding scheme that exploits the adjustment of frame rate, and at this time, the loss in temporal relation among frames due to frame deletion is compensated for the prediction of motion estimation by reusing motion vectors in the would-be deleted frames. But the processing time for transcoding don't have an improvement as much as our expectation because transcoding is done only within the transcoder. So in this paper, we propose a new transcoding algorithm based on prediction period to improve transcoding-related processing time. For this, we also modify the existing encoder so as to adjust dynamically frame rate based on the prediction period and deletion period of frames. To check how the proposed algorithm works nicely, we implement a video streaming system with the new transcoder and encoder to which it is applied. The result of the performance test shows that the streaming system with proposed algorithm improve 60% above in processing time and also PSNR have a good performance while the quality of pictures is preserved.

• Journal of Industrial Technology
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• v.27 no.B
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• pp.185-192
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• 2007

Transcoding video proxy is necessary to support various bandwidth requirements for mobile multimedia and to provide adapting video streams to mobile clients. Caching algorithms for proxy are to reduce the network traffic between the content servers and the proxy. This paper proposes a Meta-tailed caching for transcoding proxy that is efficient to lower network load and CPU load. Caching of two different data types - transcoded video, and metadata - provides a foundation to achieve superior balance between network resource and computation resource at transcoding proxies. Experimental results show that the Meta-tailed caching lowers at least 10% of CPU-load and at least 9% of network-load at a transcoding proxy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.11
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• pp.2827-2848
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• 2012

Previously, we described a social media cloud computing service environment (SMCCSE). This SMCCSE supports the development of social networking services (SNSs) that include audio, image, and video formats. A social media cloud computing PaaS platform, a core component in a SMCCSE, processes large amounts of social media in a parallel and distributed manner for supporting a reliable SNS. Here, we propose a Hadoop-based multimedia system for image and video transcoding processing, necessary functions of our PaaS platform. Our system consists of two modules, including an image transcoding module and a video transcoding module. We also design and implement the system by using a MapReduce framework running on a Hadoop Distributed File System (HDFS) and the media processing libraries Xuggler and JAI. In this way, our system exponentially reduces the encoding time for transcoding large amounts of image and video files into specific formats depending on user-requested options (such as resolution, bit rate, and frame rate). In order to evaluate system performance, we measure the total image and video transcoding time for image and video data sets, respectively, under various experimental conditions. In addition, we compare the video transcoding performance of our cloud-based approach with that of the traditional frame-level parallel processing-based approach. Based on experiments performed on a 28-node cluster, the proposed Hadoop-based multimedia transcoding system delivers excellent speed and quality.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.7
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• pp.231-240
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• 2014

Video transcoding techniques provide an efficient mechanism to make a video content adaptive to the capabilities of a variety of clients. However, it is hard to provide an appropriate quality-of-service(QoS) to the clients owing to heavy workload on transcoding operations. In light of this fact, this paper presents the dual transcoding method in order to guarantee QoS in streaming services by maximizing resource usage in a transcoding server equipped with both CPU and GPU computing units. The CPU and GPU computing units have different architectural features. The proposed method speculates workload of incoming transcoding requests and then schedules the requests either to the CPU or GPU accordingly. From performance evaluation, the proposed dual transcoding method achieved a speedup of 1.84 compared with traditional transcoding approach.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.4
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• pp.18-29
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• 2019

A dynamic adaptive streaming server consumes high processor power because it handles a large amount of transcoding operations at a time. For this purpose, multi-processor architecture is mandatory for which effective transcoding task distribution strategies are essential. In this paper, we present the design and implementation details of the transcoding workload distribution schemes at a 2-tier (frontend node and backend node) transcoding server. For this, we implemented four schemes: 1) allocation of transcoding tasks to appropriate back-end nodes, 2) task scheduling in the back-end node and 3) the communication between front-end and back-end nodes. Experiments were conducted to compare the estimated and the actual power consumption in a real testbed to verify the efficacy of the system. It also proved that the system can reduce the load on each node to optimize the power and time used for transcoding.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.6
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• pp.2785-2792
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• 2011

Such problems as delay, congestion, and crosstalk in the proxy system degrade not only QoS (Quality of Service) but responsiveness and reliability of the streaming media service. To solve this problem this paper proposed a FRTP (Fuzzy Relevance-based Transcoding Proxy) mechanism. The proposed FRTP mechanism analyzes fuzzy similarity for partitioned segment versions of media objects to create a FRTG (Fuzzy Relevance-based Transcoding Graph). Created FRTG determines the transcoding for partitioned media object segment versions. Determined transcoding improves DSR (Delay Saving Ratios), CHPR (Cache Hit Precision Ratio), and CHRR (Cache Hit Recall Ratio). The proposed mechanism is simulated to evaluate such performance parameters as DSR, CHPR, and CHRR. Simulation results shows that the proposed mechanism outperforms in DSR, CHPR and CHRR compared with the other existing mechanisms.