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IEEE Wireless Communications and Networking Conference (WCNC)

作者： Benkacem, Ilias Taleb, Tarik Bagaa, Miloud Flinck, Hannu Aalto Univ Sch Elect Engn Dept Commun & Networking Espoo Finland Nokia Bell Labs Espoo Finland

ISBN: (纸本)9781538617342

Content delivery networks (CDNs) have been widely implemented to provide scalable cloud services. Such networks support resource pooling by allowing virtual machines to be dynamically running or stopping according to current users' demands. Recently, there has been an increasing interest in Network Function Virtualization (NFV) as an emerging technology that aims to reduce cost, enable scalability and flexibility by decoupling network functions from the underlying hardware. In this regard, this paper designs a novel architecture to provide CDN Slices as a Service and that is across multiple administrative cloud domains. The architecture is aligned with the NFV Management and Orchestration (MANO) models. The proposed platform consists of three virtual network functions (VNFs), namely virtual caches, virtual video streamers, and virtual video transcoders. Regarding the latter, the paper also proposes a scheme for load balancing the transcoding tasks of the uploaded videos over a distributed network of virtual transcoders. In this article, an extensive benchmark analysis is conducted in order to study the virtual transcoding behavior in different cloud environments. The experiment evaluations provides a solid knowledge base to predict the estimated transcoding time for an optimal workload management of videos, aiming to optimize the incurred efficient cost in terms of delivery time and latency.

关键词： transcoding Streaming media Cloud computing Servers Benchmark testing Virtual machining Quality of service

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Coding Time Prediction in H.264/HEVC transcoding Using Macroblock Sizes

Coding Time Prediction in H.264/HEVC Transcoding Using Macro...

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International Workshop on Semantic Media Adaptation and Personalization (SMAP)

作者： Panos K. Papadopoulos Natalia Panagou Maria Koziri Kostas Kolomvatsos Thanasis Loukopoulos Ioannis Anagnostopoulos Computer Science and Biomedical Informatics Univ. of Thessaly Lamia Greece Computer Science and Telecommunications Univ. of Thessaly Lamia Greece Informatics and Telecommunications Univ. of Aathens Athens Greece

The continuous customers' demand for higher resolution video led to the development of video coding standards that surpass the limitations of H.264/AVC. Prominent examples in this category include the High Efficiency Video Coding (HEVC) and AV1 which both nominally target at least 50% better compression rate compared to H.264/AVC. However, with a plethora of videos already existing in H.264/AVC format and the majority of cameras in use supporting the older standard, the need for efficient transcoding of H.264/AVC videos into newer standards, cannot be overlooked. Although a significant amount of research exists on fast transcoding of H.264/AVC sequences, most of the works have focused on speeding up block level encoding, usually by taking advantage of mode and motion information that is already contained in the compressed input stream. Nevertheless, equally important to reducing per block transcoding time, is the exploitation of parallelism. Towards this end, identifying in advance the most computationally demanding regions is a prerequisite for efficient resource allocation, especially in a heterogeneous computing environment. In this paper we study the potential of using Macroblock size to predict the most computationally demanding frame regions. Experiments with commonly used benchmark video sequences illustrate the merits of our approach.

关键词： transcoding Complexity theory Standards Bit rate Social networking (online) Predictive models

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Fast H.264/AVC to HEVC transcoding Based on Compressed Domain Information

Fast H.264/AVC to HEVC Transcoding Based on Compressed Domai...

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Data Compression Conference (DCC)

作者： Zhang, Yihao Zha, Juan Chao, Hongyang Sun Yat Sen Univ Guangzhou 510000 Peoples R China Sun Yat Sen Univ Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou Peoples R China

ISBN: (纸本)9781538648834

One of the most important issue in applying High Efficiency Video Coding (HEVC) is how to fast transcode data from H.264/AVC to HEVC, because there are a large amount of H.264/AVC devices and videos. In this paper, unlike the traditional transcoding algorithms and other existing methods, we present a novel fast transcoding approach by exploiting the temporal and spatial information in the compressed domain which comes from both the input H.264/AVC and the previously-encoded HEVC bitstream. To solve the key issue of enhancing the relevance between compressed domain and the decisions making process in HEVC, the basic features, such as motion vectors, dividing depths and bit allocations, are further processed and composed into complex features. In the re-encoding process, an approach based on Bayesian rule is utilized to help fast decide the coding unit size and partition mode with above features. Experimental results show that this proposed transcoding approach can significantly reduce the computational complexity while retaining high coding efficiency.

关键词： transcoding Decision making Bit rate High efficiency video coding Feature extraction Videos

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Scheduling Video transcoding Jobs in the Cloud 11

Scheduling Video Transcoding Jobs in the Cloud

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IEEE International Congress on Cybermatics / IEEE Conferences on Internet of Things, Green Computing and Communications, Cyber, Physical and Social Computing, Smart Data, Blockchain, Computer and Information Technology

作者： Oikonomou, Panagiotis Koziri, Maria G. Tziritas, Nikos Dadaliaris, Antonios N. Loukopoulos, Thanas S. Stamoulis, Georgios, I Khan, Samee U. Univ Thessaly Comp Sci Lamia Greece Univ Thessaly Comp Sci & Biomed Informat Lamia Greece Univ Thessaly Elect & Comp Engn Volos Greece North Dakota State Univ Elect & Comp Engn Fargo ND USA

ISBN: (纸本)9781538679753

Video transcoding is the process of producing from an original (already encoded) input video sequence, multiple output sequences, each at potentially different bitrate, resolution and/or format. transcoding is essential to support video delivery towards clients that use different players and have different network access capabilities. In the most basic scheme the input sequence is decoded and then re-encoded at the desired levels. Although significant research on fast transcoding schemes exists, the transcoding process is still computationally intensive. For this reason efficient scheduling methods that allocate resources to transcoding jobs are necessary in order to achieve good overall performance. Such policies usually aim at allocating transcoding jobs over co-located servers, thus, they typically overlook parameters such as network traffic. Motivated by the case of transcoding in the Cloud, in this paper we investigate the problem of scheduling transcoding jobs over a distributed system comprising of processing nodes that are geographically dispersed and might be whole clusters or even separate data centers. We propose algorithms to minimize both the inter-node network traffic and the intra-node energy consumption, while meeting the deadlines and quality requirements. Through simulation experiments we conclude on the best alternatives.

关键词： video coding transcoding scheduling Cloud energy efficiency

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Fast Intra Mode Decision Algorithm for 3D-HEVC transcoding 13th

Fast Intra Mode Decision Algorithm for 3D-HEVC Transcoding

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13th International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP)

作者： Feng, XiuJun Liu, PengYu Jia, KeBin Beijing Lab Adv Informat Networks Beijing Peoples R China Beijing Univ Technol Fac Informat Technol Beijing Peoples R China

ISBN: (纸本)9783319638591;9783319638584

To satisfy the popularity of 3D application, 3D-HEVC has been developed as a new video encoding standard for multiviews. What's more, as the development of internet and mobile services, the use of 3D video application from the big screen to mobile devices become an inevitable trend. So an efficient transcoding for 3D videos is necessary. In 3D-HEVC, 3D video is comprised by multiview video and corresponding depth maps. The computational complexity is further increased caused by depth maps. In order to reduce the computational complexity caused by the mode decision for depth maps, an efficient fast intra mode decision for 3D-HEVC depth maps down-sizing video transcoding based on SVM (support vector machines, SVM) is proposed in this paper. Compared with the HTM16.0 which employs the original intra mode decision, the proposed algorithm can save about 16% of transcoding time without distortion of synthesized intermediate views.

关键词： 3D-HEVC Depth Modeling Mode (DMM) Down-sizing SVM transcoding

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Virtualized Dynamic transcoding service for adaptive streaming video over HTTP in 5G systems

Virtualized Dynamic transcoding service for adaptive streami...

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International Conference on Smart Communications in Network Technologies (SaCoNeT)

作者： Meyerson, Einar Guija, Daniel Siddiqui, Muhammad Shuaib Gomez, David Fraile, Isaac Fundacio i2CAT Barcelona Spain

ISBN: (纸本)9781538694930

SDN and NFV technologies promise to enable the flexibility and programmability of networks in 5G infrastructures to ensure lower cost of network and service provisioning and operation and to reduce the time to market for new services. This paper focuses on showcasing and assessing a virtualized dynamic transcoding service using the capabilities of a 5G system, the NFV-based SONATA framework, an EC funded project inside Horizon 2020 and 5G-PPP programs, while enhancing a virtual Content Delivery Network (CDN) service with features like elasticity and programmability. The business case of CDNs is well established where a series of business relationships are affected by various deployment scenarios that are possible within the current setting. SONATA framework allows to develop and deploy enhanced virtual services, through its Network Service Development Kit and Service Platform using a continuous integration and delivery DevOps methodology that allow high levels of programmability and flexibility to orchestrate and manage Network Services. In this way, performed experiments will demonstrate how online transcoding services deployed through the SONATA Service Platform results in a huge amount of resource saving comparing it with the commonly used offline transcoding service.

关键词： MPEG-DASH HTTP adaptive streaming virtual network functions transcoding multimedia quality of experience 5G NFV MANO microservices VNF DevOps

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An Architectural Approach for QoS Aware Video Scheduling for transcoding in Clouds

An Architectural Approach for QoS Aware Video Scheduling for...

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IEEE International Symposium on Smart Electronic Systems (iSES)

作者： Kalyan Baital Amlan Chakrabarti Kolkata Centre National Institute of Electronics and Information Technology Kolkata India A. K. Choudhury School of IT University of Calcutta Kolkata India

ISBN: (数字)9781728146553

ISBN: (纸本)9781728146560

Due to the heterogeneous networks, devices, and user preferences different versions of a source video concerning resolution, frame rate, etc. are required to be generated. transcoding is the process of converting video file from one format to another format, which is a time-consuming and challenging task. A large-scale cluster of transcoding servers is necessary for video transcoding of a large number of on-demand videos. In addition, a large amount of space is required for storage of multiple transcoded versions. Cloud computing presents a scalable framework for online video transcoding and infrastructure as a service (IaaS) cloud provides virtual machines (VMs) for creating a dynamically scalable cluster of servers. We propose a cloud-based multicore heterogeneous scheduling framework for allocation of different types of video to different types of VMs by extending the existing work on heterogeneous multicore scheduling such a way that the efficiency of the cloud based transcoding system is improved. Further we improve the proposed model by incorporating QoS requirements of video transcoding where video delay and video rejection are reduced to almost zero. The experimental results reveal that the proposed model achieve nearly 100% throughput maintaining high quality of the video by reducing video delay to almost zero.

关键词： Big Core Clouds Heterogeneous Scheduling Multicore QoS Real Time Task Small Core Task Scheduling Video transcoding Videotapes transcoding Clouds Multi-core processors TASK SCHEDULING Quality of service

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Efficient Inter-Prediction transcoding Algorithm from H.264/AVC to HEVC

Efficient Inter-Prediction Transcoding Algorithm from H.264/...

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作者： Xiangqun Li Jing Zhao Mingliang Gao Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education College of electrical engineering Northwest Minzu University The hospital Northwest Minzu University

With the development of the High Efficiency Video Coding(HEVC) video compression standard,there is an urgent need for more research on video format transcoding from H.264/Advanced Video Coding(AVC) to *** this paper,we highlight the difference between the inter-prediction mechanisms of the two standards,and propose detailed steps to complete bit stream transcoding from H.264/AVC to *** the transcoding process,we use information extracted from the H.264/AVC decoder in conjunction with our proposed algorithm,called "merging smaller blocks into a larger one"(MSBLO),to accurately obtain information regarding the coding unit(CU) size and the motion vector(MV),along with the prediction mode of the prediction unit(PU).This ultimately reduces the computational complexity of the determination of the optimum inter-prediction mode in the transcoding *** results show that our proposed transcoding algorithm reduces the computational complexity of the current HEVC model by approximately 90% with a lower rate distortion loss of coding efficiency.

关键词： H.264/AVC HEVC video coding transcoding inter-prediction

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transcoding of Next Generation Distributed Video Codec for Mobile Video

Transcoding of Next Generation Distributed Video Codec for M...

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International Conference on Advances in Electronics, Computers and Communications (ICAECC)

作者： Vijay Kumar Kodavalla VLSI Technology Practice Group Product Engineering Services Wipro Technologies Bangalore India

Presently most popular and widely adopted video coding standards include MPEGx, H.26x, DivX, AVSx and VPx. These standards are based on predictive video coding method, where encoder is designed to be more complex (about 10x) than the decoder. These predictive video coding standards are suitable for existing applications, including broadcast and video-ondemand. However, there are other evolving applications, including wireless surveillance, multi-view, one-time-use video cameras and mobile video, which demands encoder complexity being lower. This is in contrast to the existing video coding standards. To cater to these evolving applications, “Distributed Video Coding” (DVC) has progressed as an inventive solution. The uniqueness of distributed video coding solution is its deployment of channel coding methods into the source-coding problem, in reducing encoder complexity, even at the expense of increase in complexity of the decoder. Never the less, for a sub-set of evolving applications (for e.g., mobile video), both encoder and decoder need to be of lower complexity. For this video conference application, even the DVC is also not suitable. This paper demonstrates a method for achieving low complexity encoder and decoder from the end-user point of view, by using transcoding of DVC and H.264 Advanced Video Codec (AVC) in the network. The elaborate research and proof-of-concept models developed, over a period of several years, have become the basis for this paper.

关键词： Decoding Streaming media Video coding Discrete cosine transforms transcoding Complexity theory

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QoS-Aware Resource Allocation for Video transcoding in Clouds

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2017年第1期27卷 49-61页

作者： Wei, Lei Cai, Jianfei Foh, Chuan Heng He, Bingsheng Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore Univ Surrey Ctr Commun Syst Res Guildford GU2 7XH Surrey England Natl Univ Singapore Singapore Singapore

As the biggest big data, video data streaming in the network contributes the largest portion of global traffic nowadays and in the future. Due to heterogeneous mobile devices, networks, and user preferences, the demands of transcoding source videos into different versions have increased significantly. However, video transcoding is a time-consuming task, and how to guarantee quality-of-service (QoS) for large video data is very challenging, particularly for those real-time applications that hold strict delay requirement such as live TV. In this paper, we propose a cloudbased online video transcoding (COVT) system aiming to offer economical and QoS guaranteed solution for online large-volume video transcoding. COVT utilizes the performance profiling technique to obtain the different performances of transcoding tasks in different infrastructures. Based on the profiles, we model the cloud-based transcoding system as a queue and derive the QoS values of the system based on the queuing theory. With the analytically derived relationship between QoS values and the number of CPU cores required for transcoding workloads, COVT is able to solve the optimization problem and obtain the minimum resource reservation for specific QoS constraints. A task scheduling algorithm is further developed to dynamically adjust the resource reservation and schedule the tasks so as to guarantee the QoS in runtime. We implement a prototype system of COVT and experimentally study the performance on real-world workloads. Experimental results show that the COVT effectively provisions a minimum number of resources for predefined QoS. To validate the effectiveness of our proposed method under large-scale video data, we further perform simulation evaluation, which again shows that the COVT is capable of achieving economical and QoS-aware video transcoding in cloud environment.

关键词： Cloud computing resource allocation video transcoding

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