OpenStack Storm驱动的HEVC云实时转码平台优化

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本篇硕士论文探讨了基于OpenStack Storm技术的高效视频编码（HEVC）云转码平台在分布式计算环境中的应用。随着数字视频分辨率需求日益增长，现代世界中视频在各种多媒体设备上广泛应用，高清视频的互联网传输对带宽需求极高，这促使寻求压缩技术来减小文件大小，去除冗余和细节信息，从而降低传输需求。 HEVC（High Efficiency Video Coding）作为新一代视频编码标准，相较于先前的标准如AVC（Advanced Video Coding），在保持相同画质的情况下能提供更高的压缩效率。实时性和低延迟对于现代多媒体服务至关重要，尤其是在直播和远程视频会议中。因此，研究者提出了一种利用云计算资源，特别是OpenStack Storm这样的分布式计算框架，来实现低延迟视频转码服务的解决方案。 OpenStack Storm是一个开源的分布式计算平台，它提供了灵活的资源管理和调度能力，有助于将复杂的视频编码任务分解到多个节点上并行处理，显著缩短了视频压缩的时间消耗。通过OpenStack Storm，可以实现动态扩展和优化资源分配，确保在高峰期也能提供高效的转码性能。论文作者Raisul Bhuiyan在指导下，深入研究了如何将HEVC编码技术与OpenStack Storm结合，设计并构建了一个可扩展的云转码系统。系统不仅能够处理高分辨率和高帧率的视频，还注重了实时性要求，满足了现代用户对流畅、高质量视频体验的需求。此外，论文还可能涵盖了转码策略、错误容错机制、以及如何在OpenStack Storm架构中实现视频流的高效分发等关键知识点。论文的结论部分可能会分析这种解决方案的优势，包括成本效益、性能提升以及对现有基础设施的兼容性，同时探讨未来可能的改进方向和挑战。这篇2016年的硕士论文是云计算与视频编码技术领域的重要研究成果，为行业提供了实现高效、低延迟视频转码服务的新思路和实践案例。

jitter in the start of the video. To avoid these issues, the transcoding process is done

using several powerful platform and software in the network and then transmitted over

the internet to the playing device. This process of using multiple connected devices

in the network is known as distributed computing. This distributed computing system

could be using a couple of powerful CPUs in the same network or a cloud computing

which will be discussed in chapter 3. Several works have been conducted on video

coding in cloud and distributed system. However, the robustness of the cloud and

network delay for communication between the systems play a big role. When we are

using system with higher number of processing unit, we have lower delay which can

provide a good control over the trafﬁc but the disadvantages of such system is higher

cost and power consumptions.

1.2 Related Work

Video coding standards H.265 or HEVC is the latest codec which has much higher

complex computational requirements compare to its predecessor codec H.264 or AVC.

HEVC aims to reduce half of the bitrate for AVC codec [9], hence it requires more

processing unit as compared to any previous video codec. This is why parallel trans-

cocding become very important research area. Parallel distributed computing for video

transcoding has been trending since the beginning of video transcoding. A good num-

ber of research work have been conducted to explore parallelization that comes with

the coding standard. The coding standard has improved parallelization with time. Fur-

thermore there were several attempts to use multi-core or many core computers with

parallelized transcoder. However, cloud computing has become more acceptable due

to several advantageous terms such as cost, performance and higher level of paralleliz-

ation [10, 11].

The authors of [10, 12, 13] has worked with video partitioning methods for HEVC

encoding. The author proposes three stages for the video transcoding namely Partition-

ing stage, Transcoding stage and Combining stage. The video partitioner segmentize

the uncompressed video ﬁle into multiple non-overlapping segment. This stage only

recognise the start and end point of a video by using the video partitioning algorithm

provided by the user. This stage also downsize the network transmission overhead by

partitioning video and sending them to the transcoding stage where these segmented

ﬁles will be mapped. The author of [10] described Uniform and GOP based video par-

titioning. These experiments with GOP based transcoding in cloud computing showed

that it reduces transcoding time signiﬁcantly. It is able to decrease HEVC transcod-

ing time even with a greater computational complexity. This work was able to keep a

corresponding bitrate witha a higher number of I-frame. Although this work has very

good similarities with the research that we are conducting in Ericsson research. Our

research will also use independent GOP for distributed video transcoding. However,

we will use different platform, transcoder and distributing software for scheduling.

Authors of [9, 13] focused on picture based and coding tree based paralleliza-

tion. They explored also the Wavefront Parallel Processing (WPP) for Multi-core and

Many-core processors.The authors were aiming to transcode different part of a frame

in parallel without downgrading the quality and bitrate. These articles also focused

on WPP coding implemented on multi- and many-core processors which they called

Overlapped Wavefront (OWF). OWF processes several blocks and frame at the same

time in different processing cores. The transcoder software that we are using is ’C65’

and it is capable to use these existing parallelism.

In this paper [8] author worked with video transcoding with data level parallelism in

a cloud computing platform. This paper shows very good improvements on transcod-

ing with a dynamic scheduling to prevent overﬂow and underﬂow of the transcoded

data. It has also shown improvements for resource allocation and deallocation in the

cloud infrastructure. This work is largely similar to our research. However, the author

worked with different coding standard (such as H.264), cloud platform as well as other

parallelisms.

1.3 Problem Deﬁnition

Video coding has been around since the development of broadcast, interlacing and

recording technique of predictive compression [14, 7]. Nowadays it has grown in-

terest with modernization of the electronic devices and popularity of the on-demand

videos like online gaming and sports event. Computer gaming has higher requirements

when it comes to game precision, responsiveness and fairness. These requirements in

high performance gaming architecture guarantees good playability [15, 16]. Such on-

demand video coding has made it almost impossible for a single device to keep up

with the network delays, processing delays and increased ﬁle size. Video transcoding

is a complex system which requires several process to deliver a good quality video to

the end user with less distortion and minimized size in a required transmission speed.

It becomes a complex task for the video service provider to maintain all these stand-

ards. On the other hand, it is not also possible to get big number of processing unit

to keep encoding the video because some of the resources might not be available in

any random time. A dedicated cloud service with software and hardware makes the

service more costly. So one possible idea would be to use the devices that are already

in the network for example a home network might contain several personal computer,

laptops, tablets and phones [17]. If the network structure is capable to use the existing

devices in the private network and create a simple cloud network, the processing unit

could be increased signiﬁcantly. This simple cloud system is easily scalable and a cost

efﬁcient distributed system as it uses existing systems.

There are several ways proposed to deal with small distributed systems. It received

popularity due to the fact that it is possible to control very easily with efﬁcient and

optimized software to minimize arithmetic processes. It is easily scalable with minimal

extra cost as the devices already exists in the network. However, this kind of process

might require constant monitoring of the process and the reliability of the software will

play a big role for a better outcome. It is also challenging for the following reasons:

• Availability of the resources: As the distributive system means the use of sev-

eral available system it will require processing units to be connected to the host

device continuously. Any kind of interruption on the communication might lead

to data loss and make the process unstable. This kind of error will create more

delays to the coding process which probably could take more time and produce

unsatisfactory results than the serial implementation. So the transcoding process

will require a dynamic resource management to deal with this kind of network

or resource failure where the software should be able to reassign the failed tasks

that were allocated for the failed device(s).

• Supportability for request: Input videos (raw videos) are segmented in a shorter

length in order to avoid memory overﬂow in the devices. Moreover, the devices

with smaller memory size will not be able to allocate the required memory for

unsegmented video ﬁle. In additon, the host device sends the bitstream with a

speciﬁc structure and an architecture of the decoding processing. We also need

to consider that some of these devices will not be able to support the bitstream

or fail to follow the instructions from the main device.

• Scheduling: We are also considering the scheduling process for the task distri-

bution. The host devices should be aware about the available connected devices.

Some of the connected devices might not be available as it has other tasks to

complete. In addition, the scheduler should be able to know which device is

responsible for what kind of tasks and what type of communication packet it

should receive form the other devices. So, simple Scheduler takes part in load

balancing and task distribution, which makes it complicated to create Scheduler

for distributed systems.

• Platforms: The idea of distributive computing is fairly new and still develop-

ing. These are platforms that have not been adopted to this concept of sharing

platforms in distributive computing. Moreover, the ideal distributive systems

usually uses data to be stored and processed but this concept of processing bit-

stream should be dealt in the new devices.

1.4 Objective of the thesis

The main objective of the thesis is to analyze the complexity of distributed computing

such as Task distribution, Scheduling, Resource allocation and so on. These analysis

will be with different architectures like x86 (32-bit) and x64 (64-bit) instruction set and

operating systems like MS Windows and GNU/ Linux and compare the results. For

this purpose, ﬁle or segment based video will be used to analyze the video encoding

and transcoding process in C65 which is a video coding software developed in Ericsson

research. C65 is capable to encode and transcode video ﬁles to H.265. This software

can use all sorts of a parallelization and has the capability to use multi-threading and

Graphical Processing Unit (GPU). The sub-goal of this thesis is to familiarize with the

C65 and ﬁnd the efﬁciency of this software in the distributed systems. The research

will explore the GOP based video and amplify the data-level parallelism. The ﬁnal

work of this thesis should also be able to monitor the process in all stages from reading

ﬁle to playing the bitstream using a dash player from videos saved by a HTTP server.

The research work also requires to install and conﬁgure Apache Storm in both of the

operating systems so that the host in a storm cluster can read input ﬁles directly from

one or several cameras. Later, we will discuss how to conﬁgure storm and the topology

design so that the storm cluster can run without any errors. Apache storm monitors

each processes and report back to the user using a web UI and the work described in this

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