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A big video data transcoding service for social media over federated clouds

MULTIMEDIA TOOLS AND APPLICATIONS 2020年第13-14期79卷 9037-9061页

作者： Panarello, Alfonso Celesti, Antonio Fazio, Maria Puliafito, Antonio Villari, Massimo Univ Messina Dept Engn Messina Italy

Nowadays, the advent of social networks have revolutionised the traditional communication media. In recent years, the number of social media providers has rapidly grown. In this context, one of the major problems is the on-demand video streaming provisioning. In fact, more and more users require to post and access in real-time videos from anywhere in a short time. Therefore, a denial of service condition can cause for social media providers a loss of users and a consequent lose of money. Commonly, videos, before to be delivered, must be transcoded in order to fits both users' hardware/software device and network capabilities, raising a big video data processing issue. In order to address such a concern, in this paper, we propose a Cloud federation system that enables social media providers to work together so as to take the advantages of a scalable video processing service. Experimental results demonstrate how the overhead due to setup and maintenance tasks of the federated environment is negligible compared to the benefits in terms of video transcoding performance. Moreover, we also demonstrate how Cloud federation can lighten and speed up the whole video processing service, by introducing an additional parallelization level.

关键词： video transcoding Social media Cloud computing Federation

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Matrix encoding for data hiding using multilayer video coding and transcoding solutions

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SIGNAL PROCESSING-IMAGE COMMUNICATION 2012年第9期27卷 1025-1034页

作者： Shanableh, Tamer Amer Univ Sharjah Dept Comp Sci & Engn Sharjah U Arab Emirates

This paper proposes a multitier data hiding solution in MPEG video using matrix encoding. In the first tier of the proposed solution, raw video is encoded into two layers using multilayer Signal to Noise Ratio (SNR) encoding. During the multilayer encoding process, the quantization scales and coding parameters are stored. In the second tier, message bits are embedded in the stored quantization scales using matrix encoding. In the third and last tier, the raw video is encoded again into two layers using the modulated quantization scales and coding parameters of the first encoder. The proposed multitier solution is extended by replacing the multilayer encoder with a multilayer transcoder to accommodate situations in which the video exists in pre-coded format only. By the use of multilayer encoder or transcoding, we show that the message payload can be doubled without adversely affecting the coding quality or bitrate of the host video. We also show that message embedding using matrix encoding can be extended to modulate motion vectors. The paper also proposes two solutions for motion vector modulation based on the availability of the raw video. In comparison to existing work, the proposed solutions have higher message payloads, lower degradation in video quality and result in reduced excessive bitrate. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Data hiding Steganography Matrix encoding video transcoding Scalable video coding

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Efficient mode decision for noisy video transcoding

Efficient mode decision for noisy video transcoding

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Anna Zhang Zhen Han Zhongyuan Wang State Key Lab of Software Engineering School of Computer Wuhan University 430072 China

ISBN: (纸本)9781509041183

In many practical transcoding applications, such as video surveillance, the source videos are often contaminated by noise. The presence of noise not only results in poor compression efficiency and visual quality, but also imposes an adverse effect on the performance of subsequent video analysis tasks. Thereby it is very necessary to denoise the video. In this paper, we propose an efficient mode decision method for H.264 noisy video transcoding through analysing the effect of noise on mode decision. In the algorithm, we use the information available from previously decoded MBs to decide which modes can be overpassed with little loss to the rate-distortion performance. Experimental results show that our method saves the computational complexity nearly 65%, without noticeable rate-distortion(R-D) loss in comparison with the "Full-coding" (cascaded transcoding) method.

关键词： video transcoding Noisy video Mode decision video denosing transcoding videotapes Noise Res judicata Noise effects video monitoring

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MULTIPASS ENCODING FOR REDUCING PULSING ARTIFACTS IN CLOUD BASED video transcoding

MULTIPASS ENCODING FOR REDUCING PULSING ARTIFACTS IN CLOUD B...

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IEEE International Conference on Image Processing

作者： Yao-Chung Lin Hugh Denman Anil Kokaram Google Inc.

ISBN: (纸本)9781479983407

Low latency video transcoding is an important feature in video sharing platforms. Typically this is achieved by splitting a clip into short segments, followed by parallel encoding of the segments. However, this introduces quality artifacts due to transients in encoder rate control. We present a model for more effectively predicting the behavior of the encoder in each encoding pass, which minimizes this problem. We learn the model using measurements from more than 500 video clips, hence ensuring reliable estimation. Finally, we show that the proposed multipass strategy delivers more stable reconstructed quality for parallel video transcoding frameworks.

关键词： video transcoding Multipass rate control videotapes parallel encoding ENCODER transcoding Transient video sharing

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A Novel Development Infrastructure for Scalable video Coding/transcoding Applications

A Novel Development Infrastructure for Scalable Video Coding...

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Data Compression Conference (DCC)

作者： Movahedi, Vida Asif, Amir Chin, Alicia Amer, Ihab Hu, Zane Zhenhua Hu, Yongang York Univ Toronto ON Canada IBM Canada Markham ON Canada

ISBN: (纸本)9781509018536

Due to recent demand for playback of high quality video on mobile devices, there is the need for a scalable, error resilient framework with the ability to adjust to the network and receiver's specification. To cope with the bandwidth fluctuations in the network, the framework's scalability allows for high bit-rate video to be transcoded to a low bit-rate format while preserving quality as much as possible. The new High Efficiency video Coding (HEVC/H.265) standard allows for high compression rates, however, it is computationally intensive. We propose a novel Development Infrastructure for video coding/transcoding Applications (DIVA). This framework is capable of providing different quality of services (e.g., Bronze, Silver, Gold), and has some error-resilient capability. Taking advantage of IBM Platform Symphony, the computationally intensive task of HEVC encoding can be distributed on available local or cloud resources. Our experiments illustrate the feasibility of this approach. © 2016 IEEE.

关键词： Distributed Systems DIVA High Efficiency video Codec (HEVC) IBM Platform Symphony Multimedia Parallel Processing video Compression video transcoding Wireless Multicast

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An Architectural Approach for QoS Aware video Scheduling for transcoding in Clouds 5

An Architectural Approach for QoS Aware Video Scheduling for...

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5th IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS)

作者： Baital, Kalyan Chakrabarti, Amlan Kolkata Ctr Natl Inst Elect & Informat Technol Kolkata India Univ Calcutta AK Choudhury Sch IT Kolkata India

ISBN: (纸本)9781728146553

Due to the heterogeneous networks, devices, and user preferences different versions of a source video concerning resolution, frame rate, etc. are required to be generated. transcoding is the process of converting video file from one format to another format, which is a time-consuming and challenging task. A large-scale cluster of transcoding servers is necessary for video transcoding of a large number of on-demand videos. In addition, a large amount of space is required for storage of multiple transcoded versions. Cloud computing presents a scalable framework for online video transcoding and infrastructure as a service (IaaS) cloud provides virtual machines (VMs) for creating a dynamically scalable cluster of servers. We propose a cloud-based multicore heterogeneous scheduling framework for allocation of different types of video to different types of VMs by extending the existing work on heterogeneous multicore scheduling such a way that the efficiency of the cloud based transcoding system is improved. Further we improve the proposed model by incorporating QoS requirements of video transcoding where video delay and video rejection are reduced to almost zero. The experimental results reveal that the proposed model achieve nearly 100% throughput maintaining high quality of the video by reducing video delay to almost zero.

关键词： Big Core Clouds Heterogeneous Scheduling Multicore QoS Real-Time Task Small Core Task Scheduling video transcoding

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A heterogeneous multi-core architectural model for video scheduling for transcoding in clouds

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International Journal of Information Technology (Singapore) 2024年第5期16卷 2831-2845页

作者： Baital, Kalyan Chakrabarti, Amlan National Institute of Electronics and Information Technology Kolkata Centre Jadavpur University Campus Kolkata 700032 India A. K. Choudhury School of Information Technology University of Calcutta JD-2 JD Block Sector III Salt Lake City Kolkata 700098 India

A cluster of transcoding servers is essential for transcoding many on-demand videos. Cloud computing presents a scalable framework for online video transcoding, and the infrastructure as a service (IaaS) cloud provides heterogeneous virtual machines (VMs) for creating a dynamically scalable cluster of servers. Heterogeneous VMs consist of small or big cores, which are assigned dynamically to allocate varying sizes of videos to the appropriate VMs for transcoding. Earlier research has proposed cloud-based heterogeneous scheduling for allocating different types of videos to different types of VMs so that the quality of service is maintained by reducing video rejection. In this paper, we propose a heterogeneous multi-core video scheduling model that additionally estimates the number of VMs and cores per VM with the variation of the number of videos to optimize the resources and cost of a cloud-based transcoding system. We further estimate the model's overhead concerning the variation in the number of videos. We conducted experiments on random videos, and experimental results reveal that the proposed model provides an excellent estimation of the number of VMs and cores. The proposed model reduces the average cost by 5% and requires almost 10% fewer cores for processing video tasks than the existing work in average cases. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Big core Clouds Heterogeneous scheduling Multi-core QoS Real-time task Small core Task scheduling video scheduling video transcoding

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An Architectural Approach for QoS Aware video Scheduling for transcoding in Clouds

An Architectural Approach for QoS Aware Video Scheduling for...

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IEEE International Symposium on Smart Electronic Systems (iSES)

作者： Kalyan Baital Amlan Chakrabarti Kolkata Centre National Institute of Electronics and Information Technology Kolkata India A. K. Choudhury School of IT University of Calcutta Kolkata India

ISBN: (数字)9781728146553

ISBN: (纸本)9781728146560

关键词： Big Core Clouds Heterogeneous Scheduling Multicore QoS Real Time Task Small Core Task Scheduling video transcoding videotapes transcoding Clouds Multi-core processors TASK SCHEDULING Quality of service

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Content-aware error-resilient transcoding using prioritized intra-refresh for video streaming

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2005年第4-5期16卷 563-588页

作者： Chiou, HJ Lee, YR Lin, CW Natl Chung Cheng Univ Dept Comp Sci & Informat Engn Chiayi 621 Taiwan

Transmitting video data over wireless networks can be very unreliable due to packet-loss, leading to serious video quality degradation which is annoying to human perception. The lost packets not only affect the quality of Current frame, but also lead to error propagation to subsequent frames due to motion-compensated prediction techniques used in standard video codecs. Adding error-resilience to a video bitstream for robust video delivery to users thus becomes a very important issue. In this work, we propose a two-pass intra-refresh transcoding scheme for inserting error-resilience features to a compressed video at the media gateway of a three-tier streaming system. The proposed transcoder can adaptively vary the intra-refresh rate according to the video content and the channel's packet-loss rate to protect the most important macroblocks (MBs) against packet loss. In the first-pass encoding, the encoder estimates the amount of error propagation at MB level, and then generates side information as transcoding hints for use at the transcoder. In the second-pass transcoding, the error-resilient transcoder adaptively determines the intra-refresh rate and the locations of MBs to perform intra-refresh according to the side information. Experimental results show that the proposed method can effectively mitigate the error propagation due to packet loss so as to improve the visual quality significantly. The proposed transcoder can also meet the real-time requirement. (c) 2005 Elsevier Inc. All rights reserved.

关键词： video coding video transcoding error-resilient coding intra-refresh video streaming

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When Crowd Meets Big video Data: Cloud-Edge Collaborative transcoding for Personal Livecast

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2020年第1期7卷 42-53页

作者： Zhu, Yifei He, Qiyun Liu, Jiangchuan Li, Bo Hu, Yueming Simon Fraser Univ Sch Comp Sci Burnaby BC V5A 1S6 Canada Tableau Software Vancouver BC V6C 3E8 Canada Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China South China Agr Univ Coll Nat Resources & Environm Guangzhou 510640 Peoples R China

Deep penetration of personal computing devices and high-speed Internet has enabled everyone to be a broadcaster. In this crowdsourced live streaming service, numerous amateur broadcasters lively stream their video contents to viewers around the world. Consequently, these broadcasters generate a massive amount of video data. The set of video sources and recipients are big as well, so are demand for the storage and computational resources. transcoding becomes a must to better service these viewers with different network and device configurations. However, the massive amount of video data contributed by countless channels even makes cloud significantly expensive for providing transcoding services to the whole community. In this paper, inspired by the paradigm of Edge Computing, we propose a Cloud-edge collaborative system which combines the idle end-viewers' resources with the cloud to transcode the massive amount of videos at scale. Specifically, we put forward tailored viewer selection algorithms after empirically analyses the viewer behavior data. In the meantime, we propose auction-based payment schemes to motivate these viewers participating in the transcoding. Large-scale trace-driven simulations demonstrate the superiority of our approach in cost reduction and service stability. We further implement a prototype in PlanetLab to prove the feasibility of our design.

关键词： Cloud computing edge computing video transcoding algorithm design

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