A Cloud-Based Distributed Architecture to Accelerate Video Encoders

Aguado, J.; Peña-Ortiz, Raúl; García-Pineda, Miguel; Claver, José M.

doi:10.3390/app10155070

Cited by 3 publications

(3 citation statements)

References 23 publications

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“…Supposing that there are M training samples, the prediction values in the B1, B2, and B3 branches are shown as y * B1 (k) (6) by minimizing the loss function, the CNN-CBAM can be trained in this manner.…”

Section: Loss Function Of Proposed Cnn-cbammentioning

confidence: 99%

“…Therefore, reducing the complexity of quad-tree CU partitioning without compromising coding efficiency is a much-anticipated problem in the field of video compression. Currently, complexity reduction methods are mainly categorized into heuristics and learning-based methods, and there are also individual papers that have proposed other ways to reduce the coding time, like in [5,6].…”

Section: Introductionmentioning

confidence: 99%

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Reducing Video Coding Complexity Based on CNN-CBAM in HEVC

Li,

Wei,

Wang

et al. 2023

Applied Sciences

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High-efficiency video coding (HEVC) outperforms H.264 in coding efficiency. However, the rate–distortion optimization (RDO) process in coding tree unit (CTU) partitioning requires an exhaustive exploration of all possible quad-tree partitions, resulting in high encoding complexity. To simplify this process, this paper proposed a convolution neural network (CNN) based optimization algorithm combined with a hybrid attention mechanism module. Firstly, we designed a CNN compatible with the current coding unit (CU) size to accurately predict the CU partitions. In addition, we also designed a convolution block to enhance the information interaction between CU blocks. Then, we introduced the convolution block attention module (CBAM) into CNN, called CNN-CBAM. This module concentrates on important regions in the image and attends to the target object correctly. Finally, we integrated the CNN-CBAM into the HEVC coding framework for CU partition prediction in advance. The proposed network was trained, validated, and tested using a large scale dataset covering various scenes and objects, which provides extensive samples for intra-frame CU partition prediction in HEVC. The experimental findings demonstrate that our scheme can reduce the coding time by up to 64.05% on average compared to a traditional HM16.5 encoder, with only 0.09 dB degradation in BD-PSNR and a 1.94% increase in BD-BR.

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Section: Loss Function Of Proposed Cnn-cbammentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reducing Video Coding Complexity Based on CNN-CBAM in HEVC

Li,

Wei,

Wang

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…To adapt the number of encoders to the needs of video streaming, such as high video resolutions with bandwidth constraints and quality requirements, new Cloud-based solutions have been proposed. Gutiérrez-Aguado et al [8,9] propose a Cloud-based distributed architecture, tuned for video coding (using HEVC, VP9 and AV1 encoders), that relies on an elastic pool of workers and media servers to provide faulttolerance. This solution allows the adaptation of the resources to the workload, offers high availability, and provides ubiquitous access.…”

Section: Related Workmentioning

confidence: 99%

Event-Driven Serverless Pipelines for Video Coding and Quality Metrics

et al. 2023

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Nowadays, the majority of Internet traffic is multimedia content. Video streaming services are in high demand by end users and use HTTP Adaptive Streaming (HAS) as transmission protocol. HAS splits the video into non-overlapping chunks and each video chunk can be encoded independently using different representations. Therefore, these encode tasks can be parallelized and Cloud computing can be used for this. However, in the most extended solutions, the infrastructure must be configured and provisioned in advance. Recently, serverless platforms have made posible to deploy functions that can scale from zero to a configurable maximum. This work presents and analyses the behavior of event-driven serverless functions to encode video chunks and to compute, optionally, the quality of the encoded videos. These functions have been implemented using an adapted version of embedded Tomcat to deal with CloudEvents. We have deployed these event-driven serverless pipelines for video coding and quality metrics on an on-premises serverless platform based on Knative on one master node and eight worker nodes. We have tested the scalability and resource consumption of the proposed solution using two video codecs: x264 and AV1, varying the maximum number of replicas and the resources allocated to them (fat and slim function replicas). We have encoded different 4K videos to generate multiple representations per function call and we show how it is possible to create pipelines of serverless media functions. The results of the different tests carried out show the good performance of the serverless functions proposed. The system scales the replicas and distributes the jobs evenly across all the replicas. The overall encoding time is reduced by 18% using slim replicas but fat replicas are more adequate in live video streaming as the encoding time per chunk is reduced. Finally, the results of the pipeline test show an appropriate distribution and chaining among the available replicas of each function type.

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Cloud media video encoding: review and challenges

Moina-Rivera,

Garcia-Pineda,

Gutiérrez-Aguado

et al. 2024

Multimed Tools Appl

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In recent years, Internet traffic patterns have been changing. Most of the traffic demand by end users is multimedia, in particular, video streaming accounts for over 53%. This demand has led to improved network infrastructures and computing architectures to meet the challenges of delivering these multimedia services while maintaining an adequate quality of experience. Focusing on the preparation and adequacy of multimedia content for broadcasting, Cloud and Edge Computing infrastructures have been and will be crucial to offer high and ultra-high definition multimedia content in live, real-time, or video-on-demand scenarios. For these reasons, this review paper presents a detailed study of research papers related to encoding and transcoding techniques in cloud computing environments. It begins by discussing the evolution of streaming and the importance of the encoding process, with a focus on the latest streaming methods and codecs. Then, it examines the role of cloud systems in multimedia environments and provides details on the cloud infrastructure for media scenarios. After doing a systematic literature review, we have been able to find 49 valid papers that meet the requirements specified in the research questions. Each paper has been analyzed and classified according to several criteria, besides to inspect their relevance. To conclude this review, we have identified and elaborated on several challenges and open research issues associated with the development of video codecs optimized for diverse factors within both cloud and edge architectures. Additionally, we have discussed emerging challenges in designing new cloud/edge architectures aimed at more efficient delivery of media traffic. This involves investigating ways to improve the overall performance, reliability, and resource utilization of architectures that support the transmission of multimedia content over both cloud and edge computing environments ensuring a good quality of experience for the final user.

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A Cloud-Based Distributed Architecture to Accelerate Video Encoders

Cited by 3 publications

References 23 publications

Reducing Video Coding Complexity Based on CNN-CBAM in HEVC

Reducing Video Coding Complexity Based on CNN-CBAM in HEVC

Event-Driven Serverless Pipelines for Video Coding and Quality Metrics

Cloud media video encoding: review and challenges

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