EMES: Efficient Multi-Encoding Schemes for HEVC-based Adaptive Bitrate Streaming

Author:

Menon Vignesh V1,Amirpour Hadi1,Ghanbari Mohammad2,Timmerer Christian1

Affiliation:

1. Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt, Austria

2. School of Computer Science and Electronic Engineering, University of Essex, UK

Abstract

In HTTP Adaptive Streaming  (HAS), videos are encoded at multiple bitrates and spatial resolutions ( i.e. , representations ) to adapt to the heterogeneity of network conditions, device attributes, and end-user preferences. Encoding the same video segment at multiple representations increases costs for content providers. State-of-the-art multi-encoding schemes improve the encoding process by utilizing encoder analysis information from already encoded representation(s) to reduce the encoding time of the remaining representations. These schemes typically use the highest bitrate representation as the reference to accelerate the encoding of the remaining representations. Nowadays, most streaming services utilize cloud-based encoding techniques, enabling a fully parallel encoding process to reduce the overall encoding time. The highest bitrate representation has the highest encoding time than the other representations. Thus, utilizing it as the reference encoding is unfavorable in a parallel encoding setup as the overall encoding time is bound by its encoding time. This paper provides a comprehensive study of various multi-rate and multi-encoding schemes in both serial and parallel encoding scenarios. Furthermore, it introduces novel heuristics to limit the Rate Distortion Optimization  (RDO) process across various representations. Based on these heuristics, three multi-encoding schemes are proposed, which rely on encoder analysis sharing across different representations: (i) optimized for the highest compression efficiency , (ii) optimized for the best compression efficiency-encoding time savings trade-off , and (iii) optimized for the best encoding time savings . Experimental results demonstrate that the proposed multi-encoding schemes (i) , (ii) , and (iii) reduce the overall serial encoding time by 34.71%, 45.27%, and 68.76% with a 2.3%, 3.1%, and 4.5% bitrate increase to maintain the same VMAF, respectively compared to stand-alone encodings. The overall parallel encoding time is reduced by 22.03%, 20.72%, and 76.82% compared to stand-alone encodings for schemes (i) , (ii) , and (iii) , respectively.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Hardware and Architecture

Reference29 articles.

1. Towards Optimal Multirate Encoding for HTTP Adaptive Streaming

2. Hadi Amirpour , Ekrem Çetinkaya , Christian Timmerer , and Mohammad Ghanbari . 2020 . Fast Multi-rate Encoding for Adaptive HTTP Streaming. In 2020 Data Compression Conference (DCC). 358–358 . https://doi.org/10.1109/DCC47342.2020.00080 10.1109/DCC47342.2020.00080 Hadi Amirpour, Ekrem Çetinkaya, Christian Timmerer, and Mohammad Ghanbari. 2020. Fast Multi-rate Encoding for Adaptive HTTP Streaming. In 2020 Data Compression Conference (DCC). 358–358. https://doi.org/10.1109/DCC47342.2020.00080

3. Gisle Bjontegaard . 2001. Calculation of average PSNR differences between RD-curves. VCEG-M33 ( 2001 ). Gisle Bjontegaard. 2001. Calculation of average PSNR differences between RD-curves. VCEG-M33 (2001).

4. Jill Boyce Karsten Suehring Xiang Li and Vadim Seregin. 2018. JVET-J1010: JVET common test conditions and software reference configurations. Jill Boyce Karsten Suehring Xiang Li and Vadim Seregin. 2018. JVET-J1010: JVET common test conditions and software reference configurations.

5. Subjective and Objective Quality Assessment of Compressed 4K UHD Videos for Immersive Experience

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Video Super-Resolution for Optimized Bitrate and Green Online Streaming;2024 Picture Coding Symposium (PCS);2024-06-12

2. Quality-Aware Dynamic Resolution Adaptation Framework for Adaptive Video Streaming;Proceedings of the ACM Multimedia Systems Conference 2024 on ZZZ;2024-04-15

3. Preparing VVC for Streaming: A Fast Multi-Rate Encoding Approach;2023 IEEE International Conference on Visual Communications and Image Processing (VCIP);2023-12-04

4. Transcoding Quality Prediction for Adaptive Video Streaming;Proceedings of the 2nd Mile-High Video Conference;2023-05-07

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3