distributed video transcoding has been used to huge video data storage overhead and reduce transcoding delay caused by the rapid development of mobile video services. distributed transcoding can leverage the computing...
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ISBN:
(纸本)9783030859282;9783030859275
distributed video transcoding has been used to huge video data storage overhead and reduce transcoding delay caused by the rapid development of mobile video services. distributed transcoding can leverage the computing power of clusters for various user requests and diverse video processing demands. However, it imposes a remaining challenge on how to efficiently utilize the computing power of the cluster as well as achieve optimized performance through reasonable system parameters and video processing configurations. In this paper, we design a Cluster-based distributed Video transcoding System called CDVT using Hadoop, FFmpeg, and Mkvmerge to achieve on-demand video splitting, on-demand transcoding, and distributed processing, which can be applied to large scale video sharing over mobile devices. In order to further optimize system performance, we conducted extensive experiments on various data sets to find relevant factors that affect transcoding efficiency. We dynamically reconfigure the cluster and evaluate the impacts of different intermediate tasks, splitting strategies, and memory configuration strategies on system performance. Experimental results obtained under various workloads demonstrate that the proposed system can ensure the quality of transcoding tasks while reducing the time cost by up to 50%.
On one hand, since the introduction of UHD (ultra-high definition) videos, e.g., 4K and 8K videos, it is becoming more resource and time intensive to transcode videos. On the other hand, the increasing demand for vide...
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ISBN:
(纸本)9781538668085
On one hand, since the introduction of UHD (ultra-high definition) videos, e.g., 4K and 8K videos, it is becoming more resource and time intensive to transcode videos. On the other hand, the increasing demand for video streaming implies more videos need to be transcoded. These two facts motivate the need for techniques to speedup coding and transcoding time. In this paper, we propose Stride, the first distributed video transcoding system that leverages the Apache Spark big data platform. The design of Stride is transcoder agnostic, meaning it can adopt any transcoder implementation (e.g., FFMPEG) without any modification. We provide an experimental characterization of the impact of video transcoding and Spark configuration parameters to identify the optimal settings. We also compare Stride with competing approaches. Our results show that Stride achieves 3:27 times speedup when the computing power (i.e., the number of vCPUs in a cloud) is increased by a factor of 4, which is significantly higher than the other alternatives we explore. In particular, Spark's dynamic task scheduler allows Stride to reduce transcoding time by 19:86% compared to an implementation without Spark. Our benchmark study suggests that Stride can support transcoding from 4K to 1080p (full HD) at a rate matching the video bitrate using approximately only 24 virtual cores.
360 degrees Virtual Reality (VR) services with resolutions of 8K and beyond are a challenging task due to limits of both decoding complexity and constrained public internet bandwidth of consumer devices. Also, general...
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ISBN:
(纸本)9781538650417
360 degrees Virtual Reality (VR) services with resolutions of 8K and beyond are a challenging task due to limits of both decoding complexity and constrained public internet bandwidth of consumer devices. Also, general streaming servers cannot service these large-resolution video streams to many clients because of bandwidth limitation. In this paper, we propose a distributed video transcoding system for achieving viewport adaptive streaming, which is known as tiled streaming, of 8K 360 degrees VR video. The proposed system consists of many motion-constrained High Efficiency Video Coding (HEVC) encoders, a Hadoop/Spark-based distributed computing platform, light-weight bitstream stitcher, and dual HEVC decoders. Experimental results show that 8K 360 degrees videos which are split by 8x8 tiles, respectively, can be encoded at 99 fps, and 4x4 tiles are stitched at 9,585 fps, on average.
On one hand, since the introduction of UHD (ultra-high definition) videos, e.g., 4K and 8K videos, it is becoming more resource and time intensive to transcode videos. On the other hand, the increasing demand for vide...
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ISBN:
(纸本)9781538668092;9781538668085
On one hand, since the introduction of UHD (ultra-high definition) videos, e.g., 4K and 8K videos, it is becoming more resource and time intensive to transcode videos. On the other hand, the increasing demand for video streaming implies more videos need to be transcoded. These two facts motivate the need for techniques to speedup coding and transcoding time. In this paper, we propose Stride, the first distributed video transcoding system that leverages the Apache Spark big data platform. The design of Stride is transcoder agnostic, meaning it can adopt any transcoder implementation (e.g., FFMPEG) without any modification. We provide an experimental characterization of the impact of video transcoding and Spark configuration parameters to identify the optimal settings. We also compare Stride with competing approaches. Our results show that Stride achieves 3.27 times speedup when the computing power (i.e., the number of vCPUs in a cloud) is increased by a factor of 4, which is significantly higher than the other alternatives we explore. In particular, Spark's dynamic task scheduler allows Stride to reduce transcoding time by 19.86% compared to an implementation without Spark. Our benchmark study suggests that Stride can support transcoding from 4K to 1080p (full HD) at a rate matching the video bitrate using approximately only 24 virtual cores.
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