Cloud-based elastic architecture for distributed video encoding: Evaluating H.265, VP9, and AV1

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

doi:10.1016/j.jnca.2020.102782

Cited by 13 publications

(5 citation statements)

References 11 publications

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“…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%

“…All these encoding and transcoding processes require distributed systems to improve encoding times. Today, these tasks are performed in Cloud environments, as they offer the dynamism demanded by streaming platforms [9]. As we will see in Section 2, Cloud-based video encoding solutions have evolved as new paradigms have emerged in the Cloud.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Event-Driven Serverless Pipelines for Video Coding and Quality Metrics

et al. 2023

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“…AV1 (AOMedia Video 1) bit stream (bitstream) already found its way for many applications and services [2], [14]- [15]. AV1 is considered as an open-source royalty-free format, where the next-generation solution is expected to bring 30 to 50 percent bit-rate savings at the same quality level compared to Google's widely adopted VP9.…”

Section: A Video Communication and Av1 Formatmentioning

confidence: 99%

Quality assessment experiment for 4k SVT-AV1 video format

Gavrovska

Milivojević

Zajic³

2021

Telfor J

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The industrial consortium AOMedia has recently developed the AV1 (AOMedia Video 1) format considered as a successor of VP9. One of the main goals with the video coding and compression format is to enable efficient Web based video content delivery, as in the case of OTT (Over-the-top) services. Nowadays, there are different AV1 software implementations available for experimental analysis besides the reference tool libaom. The Scalable Video Technology for AV1, or SVT-AV1 CODEC, is oriented towards further optimizations for specific Intel hardware. In this paper, we have performed a video quality assessment experiment with some of the SVT-AV1 implementations using 4k like video. The results show the advantages of SVT-AV1 format, as well as its performance for different quantization factors.

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“…Related methods tackle this challenging problem by dividing a video sequence into smaller tasks, and distributing them among instances for encoding [5], [6]. These methods only focus on Cloud infrastructures, follow one objective (i.e., time or cost), and do not consider the transmission time of video data that significantly affects the total encoding time and cost.…”

Section: Introductionmentioning

confidence: 99%

MPEC2: Multilayer and Pipeline Video Encoding on the Computing Continuum

Afzal

Samani

Mehran

et al. 2022

2022 IEEE 21st International Symposium on Network Computing and Applications (NCA)

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Video streaming is the dominating traffic in today's data-sharing world. Media service providers stream video content for their viewers, while worldwide users create and distribute videos using mobile or video system applications that significantly increase the traffic share. We propose a multilayer and pipeline encoding on the computing continuum (MPEC2) method that addresses the key technical challenge of high-price and computational complexity of video encoding. MPEC2 splits the video encoding into several tasks scheduled on appropriately selected Cloud and Fog computing instance types that satisfy the media service provider and user priorities in terms of time and cost. In the first phase, MPEC2 uses a multilayer resource partitioning method to explore the instance types for encoding a video segment. In the second phase, it distributes the independent segment encoding tasks in a pipeline model on the underlying instances. We evaluate MPEC2 on a federated computing continuum encompassing Amazon Web Services (AWS) EC2 Cloud and Exoscale Fog instances distributed on seven geographical locations. Experimental results show that MPEC2 achieves 24 % faster completion time and 60 % lower cost for video encoding compared to resource allocation related methods. When compared with baseline methods, MPEC2 yields 40 %-50 % lower completion time and 5 %-60 % reduced total cost.

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Cloud-based elastic architecture for distributed video encoding: Evaluating H.265, VP9, and AV1

Cited by 13 publications

References 11 publications

Event-Driven Serverless Pipelines for Video Coding and Quality Metrics

Event-Driven Serverless Pipelines for Video Coding and Quality Metrics

Quality assessment experiment for 4k SVT-AV1 video format

MPEC2: Multilayer and Pipeline Video Encoding on the Computing Continuum

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