FAST MULTI-ENCODING TO REDUCE THE COST OF VIDEO STREAMING

2025-05-06 0 0 1.03MB 12 页 10玖币

侵权投诉

FAST MULTI-ENCODING TO REDUCE THE COST OF VIDEO

STREAMING

Hadi Amirpour1, Vignesh V Menon1, Ekrem etinkaya1, Adithyan

Ilangovan2, Christian Feldmann2, Martin Smole2, and Christian Timmerer1,2

1Christian Doppler Laboratory ATHENA, Alpen-Adria-Universitt, Klagenfurt, Austria

{firstname.lastname}@aau.at

2Bitmovin, Klagenfurt, Austria

{firstname.lastname}@bitmovin.com

ABSTRACT

The growth in video Internet traffic and advancements in video attributes such as

framerate, resolution, and bit-depth boost the demand to devise a large-scale, highly

efficient video encoding environment. This is even more essential for Dynamic

Adaptive Streaming over HTTP (DASH)-based content provisioning as it requires

encoding numerous representations of the same video content. High Efficiency

Video Coding (HEVC) is one standard video codec that significantly improves

encoding efficiency over its predecessor Advanced Video Coding (AVC). This

improvement is achieved at the expense of significantly increased time complexity,

which is a challenge for content and service providers. As various representations

are the same video content encoded at different bitrates or resolutions, the encoding

analysis information from the already encoded representations can be shared to

accelerate the encoding of other representations. Several state-of-the-art schemes

first encode a single representation, called a reference representation. During this

encoding, the encoder creates analysis metadata with information such as the slice-

type decisions, CU, PU, TU partitioning, and the HEVC bitstream itself. The

remaining representations, called dependent representations, analyze the above

metadata and then reuse it to skip searching some partitioning, thus, reducing the

computational complexity. With the emergence of cloud-based encoding services,

video encoding is accelerated by utilizing an increased number of resources, i.e.,

with multi-core CPUs, multiple representations can be encoded in parallel. This

paper presents an overview of a wide range of multi-encoding schemes with and

without the support of machine learning approaches integrated into the HEVC Test

Model (HM) and x265, respectively. Seven multi-encoding schemes are presented,

and their performance in encoding time complexity and bitrate overhead compared

to the state-of-the-art approaches are shown. Enabling fast multi-encoding for HAS

in modern Over-the-top (OTT) workflows will reduce time-to-market and costs

immensely.

INTRODUCTION

HTTP Adaptive Streaming (HAS) is the de-facto standard in delivering videos over the

internet to a variety of devices. The main idea behind HAS is to divide the video content into

segments and encode each segment at various bitrates and resolutions, called

representations, which are stored in plain HTTP servers as shown in Figure 1. These

representations are stored in order to continuously adapt the video delivery to the network

conditions and device capabilities of the client. To meet the high demand for streaming high-

quality video content over the Internet and overcome the associated challenges in HAS, the

Moving Picture Experts Group (MPEG) has developed a standard called Dynamic Adaptive

Streaming over HTTP (MPEG-DASH) [1]. The increase in video traffic and improvements in

video characteristics such as resolution, framerate, and bit-depth raise the need to develop

a large-scale, highly efficient video encoding environment [2]. This is even more crucial for

DASH-based content provisioning as it requires encoding multiple representations of the

same video content [3].

High Efficiency Video Coding (HEVC) [4] is one standard video codec that is widely being

used in content production nowadays. Based on Bitmovin’s video developer report in 2021

[5], HEVC is used in 49% of productions in 2021 and it is expected to be added to more than

25% of extra productions in 2022. HEVC significantly improves coding efficiency over its

predecessor Advanced Video Coding (AVC) [6]. This improvement is achieved at the cost

of significantly increased runtime complexity, which is a challenge for content and service

providers. As various representations of the same video content are encoded at different

bitrates or resolutions, the encoding analysis information from the

already encoded representations can be shared to accelerate the encoding of other

representations.

Several state-of-the-art schemes [6–12], first encode a single representation, called a

reference representation. The encoder creates analysis metadata (file) with information such

as the slice-type decisions [13], CU, PU, TU partitioning [14], and the HEVC bitstream itself

during this encoding. The remaining representations, called dependent representations,

analyze the above metadata and then reuse it to skip searching some partitioning, thus,

reducing the computational complexity. With the emergence of cloud-based encoding

services [15] and in live applications video encoding is accelerated by utilizing an increased

number of resources, i.e., with multi-core CPUs, multiple representations can be encoded

in parallel.

In this paper, the schemes are analyzed for both serial and parallel encoding environments.

The term multi-rate is used when all representations are encoded at a single resolution but

at different bitrates. Multi-encoding is used when a single video is provided at various

resolutions, and each resolution is encoded at different bitrates.

BACKGROUND AND RELATED WORK

In HAS, the same video content is encoded at multiple bitrates to continuously adapt the

video delivery to the users’ need, resulting in a significant increase in the encoding cost.

However, as the same content is encoded multiple times, the encoder analysis information

from the already encoded representation(s) can be reused to speed up the encoding

process of the remaining representations. In HEVC, frames are first divided into slices, and

then they are further divided into square regions called Coding Tree Units (CTU) [4], which

are the main building blocks of HEVC. To encode each CTU, it is recursively divided into

smaller square regions called Coding Units (CUs) (see Figure 2). Depth values from 0 to 3

are assigned to CU sizes from   to    pixels. Therefore, to find the best CTU

partitioning, 85 CUs including one   CU, four   CUs, sixteen   CUs, and

sixty-four  CUs are searched.

Figure 2. In HEVC, frames are divided into CTUs, and each CTU is then divided into CUs. Each CU is subdivided

into PUs, and the prediction residuals of CUs are partitioned into TUs. The optimal CTU partitioning is found after

an exhaustive search process through all CUs, PUs, and TUs.

The inter-prediction modes comprise of Merge/Skip 2N×2N, Inter 2N×2N, Symmetric Motion

Partition (SMP, including Inter 2N×N and Inter N×2N), Asymmetric Motion Partition (AMP,

including Inter 2N×nU, Inter 2N×nD, Inter nL×2N, and Inter nR×2N), and Inter N×N. In

contrast, the intra-prediction modes involve Intra 2N×2N and Intra N×N. The best PU mode

is selected according to all modes’ minimum rate-distortion cost (RD-cost). Furthermore, for

transform coding of the prediction residuals, each CU can be partitioned into multiple

Figure 1. Encoded video representations as used in DASH.  and  denote the frame width and frame height,

respectively.  denotes the number of resolutions stored. 

  and   denote the lowest and highest

resolutions, respectively.  denotes the number of bitrate representations in each resolution.  to  represent

the target bitrates in the ascending order for the representations in each resolution.

文档加载中……请稍候！
如果长时间未打开，您也可以点击刷新试试。

下载文档到电脑，查找使用更方便

10 玖币 0人已下载

立即下载

摘要：

FASTMULTI-ENCODINGTOREDUCETHECOSTOFVIDEOSTREAMINGHadiAmirpour1,VigneshVMenon1,EkremÇetinkaya1,AdithyanIlangovan2,ChristianFeldmann2,MartinSmole2,andChristianTimmerer1,21ChristianDopplerLaboratoryATHENA,Alpen-Adria-Universität,Klagenfurt,Austria{firstname.lastname}@aau.at2Bitmovin,Klagenfurt,Austri...

展开>> 收起<<

FAST MULTI-ENCODING TO REDUCE THE COST OF VIDEO STREAMING.pdf

共12页,预览3页

还剩页未读，继续阅读

声明：本站为文档C2C交易模式，即用户上传的文档直接被用户下载，本站只是中间服务平台，本站所有文档下载所得的收益归上传人(含作者)所有。玖贝云文库仅提供信息存储空间，仅对用户上传内容的表现方式做保护处理，对上载内容本身不做任何修改或编辑。若文档所含内容侵犯了您的版权或隐私，请立即通知玖贝云文库，我们立即给予删除！

FAST MULTI-ENCODING TO REDUCE THE COST OF VIDEO STREAMING

相关推荐

开通VIP享超值会员特权

作者详情

相关内容

热门标签

举报选择: