Production-ready environment for HLS Player using FFmpeg with automation on S3 Bucket using Ansible

Jain, Nitesh; Shrivastava, Harshika; Moghe, Asmita A.

doi:10.1109/idea49133.2020.9170694

Cited by 3 publications

(4 citation statements)

References 3 publications

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“…[5], libdash [11], dan MP4Box dari GPAC [12]. FFMPEG merupakan salah satu software yang dapat digunakan secara cross platform dan memiliki kemampuan yang sangat lengkap dalam mengolah audio video seperti merekam, convert dan streaming [13,14].…”

Section: Pendahuluanunclassified

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Adaptive Streaming Server dengan FFMPEG dan Golang

Kristiadi¹,

Marwiyati²

2021

RESTI

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Quality of experience (QoE) when accessing video streaming becomes a challenge in varieties of network bandwidth/speed. Adaptive streaming becomes an answer to gain good QoE. An architecture system of the adaptive streaming server with Dynamic Adaptive Streaming over HTTP (DASH) was proposed. The system was consists of two services e.g transcoding and streaming. Transcoding service encodes an audio file, multi-bitrates video files, and manifest.mpd files. Streaming service serves client streaming requests that appropriate to client network profiles. The system is built using the Golang programming environment and FFMPEG. Transcoding service has some execution mode (serial and concurrent) and passing mode (1 pass and 2 passes). The transcoding service test results show that concurrent execution is faster 11,5% than the serial execution and transcoding using 1 pass is faster 46,95% than 2 passes but the bitrate of output video lower than the determinate bitrate parameter. The streaming service has a good QoE. In the 5 scenarios, buffer level=0 happens 5 times, and its total duration is 64 seconds. Buffer level=0 happens when extreme changes happen in network speed from fast to too slow.

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Section: Pendahuluanunclassified

“…Tetapi FFMPEG membutuhkan lingkungan pemrograman untuk mengotomatisasi pengolahan audio video. Beberapa lingkungan pemrograman yang telah digunakan yaitu Javascript [5], PHP [15] dan Shell Script [14].…”

Section: Pendahuluanunclassified

Adaptive Streaming Server dengan FFMPEG dan Golang

Kristiadi¹,

Marwiyati²

2021

RESTI

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“…Implementasi transcoding dengan model komputasi terdistribusi dilaporkan menunjukkan perbaikan waktu yang signifikan dibandingkan transcoding dengan satu komputer (Song et al, 2014;Liu and Yuan, 2018;Liu et al, 2016;Chang et al, 2016;Gutiérrez-Aguado et al, 2020). Implementasi tersebut beberapa dilakukan dengan memanfaatkan framework Hadoop (Song et al, 2014;Liu et al, 2016;Sameti, Wang and Krishnamurthy, 2018;Huh, Kim and Jeong, 2019), cloud computing seperti AWS (Jiang, Lee and Zomaya, 2019;Jain, Shrivastava and Moghe, 2020) dan layanan encoding cloud service seperti Bitmovin (Trattnig, Timmerer and Mueller, 2018).…”

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“…Dari implementasi yang telah dilakukan tersebut, belum ditemui layanan transcoding dengan konsep Mapreduce yang memanfaatkan FFMPEG library, Remote Procedure Call (RPC) dan Secure Copy (SCP) dalam lingkungan pemrograman Golang. FFMPEG library digunakan untuk meng-convert data audio video dan library ini mampu dijalankan pada sistem operasi personal komputer yang berbeda (Kristiadi and Marwiyati, 2021;Jain, Shrivastava and Moghe, 2020). Komunikasi antar masterworker menggunakan RPC.…”

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Mapreduce dalam Layanan Transcoding

Kristiadi,

Marwati

2023

JTIIK

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Penyediaan file video dengan bitrate bervariasi menjadi syarat utama bagi layanan Video On Demand yang menerapkan adaptive streaming. Hal tersebut dilakukan dengan transcoding yang menghasilkan video dengan multi-bitrate. Proses multi-bitrate transcoding membutuhkan waktu yang tidak singkat. Lama durasi waktunya sebanding dengan besarnya bitrate, frame size dan frame rate. Untuk mempersingkat durasi transcoding, dibuat sebuah prototipe layanan transcoding dengan menerapkan Mapreduce. Layanan transcoding dengan Mapreduce terdiri dari satu komputer master dan beberapa komputer worker yang terhubung dalam satu LAN. Dengan dikoordinir oleh komputer master, komputer-komputer worker mengerjakan proses Map dan Reduce. Di dalam proses Map dilakukan transcoding terhadap segmented videos. Di dalam proses Reduce dilakukan penggabungan segmen-segmen video yang telah di-transcode dengan parameter/key (bitrate, frame size, dan frame rate) yang sama menjadi satu video yang utuh. Prototipe layanan transcoding dibuat menggunakan Library FFMPEG untuk transcoding, SCP untuk transfer file, RPC untuk komunikasi antar komputer. Di dalam pengujian prototipe, jumlah komputer worker ditentukan sebanyak 7 buah. Kinerja layanan transcoding sangat memuaskan dengan rata-rata efektifitas transcoding sebesar 71,3% dibandingkan dengan transcoding menggunakan satu komputer. AbstractThe provision of video files with varying bitrates is the main requirement for Video On Demand services that implement adaptive streaming. Transcoding that produces multiple bitrates achieves that. Multi-bitrate transcoding can take a longer time. That duration is comparable with the large of the video's bitrates, frame sizes, and frame rates. A transcoding service prototype was created based on Mapreduce to shorten the duration. Transcoding service using Mapreduce consists of several computers connected to a LAN, one as master and the other as worker computers. The master coordinates the workers to do the Map and Reduce process. In the Map process, workers transcode the segmented videos. In the Reduce process, workers merge all transcoded video segments with the same key or parameters (bitrate, frame size, and frame rate) into a single video. The transcoding service prototype was created using the FFMPEG library for transcoding, SCP for file transfer, and RPC for communication between computers. In the testing stage, the number of workers is 7 computers. Service performance is very satisfactory, with average transcoding effectiveness of 71,3% compared to transcoding using a single computer.

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