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Effectiveness of crypto-transcoding for H.264/AVC and HEVC video bit-streams

MULTIMEDIA TOOLS AND APPLICATIONS 2019年第15期78卷 21455-21484页

作者： Shah, Rizwan A. Asghar, Mamoona N. Abdullah, Saima Fleury, Martin Gohar, Neelam Islamia Univ Bahawalpur Dept Comp Sci & IT Bahawalpur Pakistan Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England Shaheed Benazir Bhutto Women Univ Dept Comp Sci Peshawar Pakistan

To avoid delays arising from a need to decrypt a video prior to transcoding and then re-encrypt it afterwards, this paper assesses a selective encryption (SE) content protection scheme. The scheme is suited to both recent standardized codecs, namely H.264/Advanced Video Coding (AVC) and High Efficiency Video Coding (HEVC). Specifically, the paper outlines a joint crypto-transcoding scheme for secure transrating of a video bitstream. That is to say it generates new video bitrates, possibly as part of an HTTP Adaptive Streaming (HAS) content delivery network. The scheme will reduce the bitrate to one or more lower desired bit-rate without consuming time in the encryption/decryption process, which would be the case when full encryption is used. In addition, the decryption key no longer needs to be exposed at intermediate middleboxes, including when transrating is performed in a cloud datacenter. The effectiveness of the scheme is variously evaluated: by examination of the SE generated visual distortion;by the extent of computational and bitrate overheads;and by choice of cipher when encrypting the selected elements within the bitstream. Results indicate that there remains: a content;quantization level (after transrating of an encrypted video);and codec-type dependency to any distortion introduced. A further recommendation is that the Advanced Encryption Standard (AES) is preferred for SE to lightweight XOR encryption, despite it being taken up elsewhere as a real-time encryption method.

关键词： Content protection H 264 AVC HEVC Selective encryption transcoding Transrating Video streaming

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Energy-Efficient Joint Caching and transcoding for HTTP Adaptive Streaming in 5G Networks with Mobile Edge Computing

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China Communications 2019年第7期16卷 229-244页

作者： Renchao Xie Zishu Li Jun Wu Qingmin Jia Tao Huang State Key Laboratory of Network and Switching Technology Beijing University of Posts and Telecommunications Beijing 100876 China Purple Mountain Laboratories Nanjing 211111 China Dhurakij Pundit University 110/1-4 Prachachuen Rd. Laksi Bangkok 10210 Thailand Institute of Sensing Technology and Business BUPT 214315 China

With the new promising technique of mobile edge computing (MEC) emerging, by utilizing the edge computing and cloud computing capabilities to realize the HTTP adaptive video streaming transmission in MEC-based 5G networks has been widely studied. Although many works have been done, most of the existing works focus on the issues of network resource utilization or the quality of experience (QoE) promotion, while the energy efficiency is largely ignored. In this paper, different from previous works, in order to realize the energy efficiency for video transmission in MEC-enhanced 5G networks, we propose a joint caching and transcoding schedule strategy for HTTP adaptive video streaming transmission by taking the caching and transcoding into consideration. We formulate the problem of energy-efficient joint caching and transcoding as an integer programming problem to minimize the system energy consumption. Due to solving the optimization problem brings huge computation complexity, therefore, to make the optimization problem tractable, a heuristic algorithm based on simulated annealing algorithm is proposed to iteratively reach the global optimum solution with a lower complexity and higher accuracy. Finally, numerical simulation results are illustrated to demonstrated that our proposed scheme brings an excellent performance.

关键词： mobile edge computing HTTP adaptive streaming caching transcoding energy efficiency

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Fast H.264 to HEVC transcoding: A Deep Learning Method

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IEEE TRANSACTIONS ON MULTIMEDIA 2019年第7期21卷 1633-1645页

作者： Xu, Jingyao Xu, Mai Wei, Yanan Wang, Zulin Guan, Zhenyu Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China

With the development of video coding technology, high-efficiency video coding (HEVC) has become a promising alternative, compared with the previous coding standards, for example, H.264. In general, H.264 to HEVC transcoding can be accomplished by fully H.264 decoding and fully HEVC encoding, which suffers from considerable time consumption on the brute-force search of the HEVC coding tree unit (CTU) partition for rate-distortion optimization (RDO). In this paper, we propose a deep learning method to predict the HEVC CTU partition, instead of the brute-force RDO search, for H.264 to HEVC transcoding. First, we build a large-scale H.264 to HEVC transcoding database. Second, we investigate the correlation between the HEVC CTU partition and H.264 features, and analyze both temporal and spatial-temporal similarities of the CTU partition across video frames. Third, we propose a deep learning architecture of a hierarchical long short-term memory (H-LSTM) network to predict the CTU partition of HEVC. Then, the brute-force RDO search of the CTU partition is replaced by the H-LSTM prediction such that the computational time can be significantly reduced for fast H.264 to HEVC transcoding. Finally, the experimental results verify that the proposed H-LSTM method can achieve a better tradeoff between coding efficiency and complexity, compared to the state-of-the-art H.264 to HEVC transcoding methods.

关键词： H.264 HEVC transcoding deep learning LSTM

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transcoding for Live Streaming-based on Vehicular Fog Computing: An Actor-Critic DRL Approach

Transcoding for Live Streaming-based on Vehicular Fog Comput...

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IEEE Conference on Computer Communications Workshops, INFOCOM Wksps

作者： Fang Fu Yunpeng Kang Zhicai Zhang F. Richard Yu School of Physics and Electronic Eng. Shanxi University Taiyuan Shanxi P. R. China Carleton University Ottawa ON Canada

ISBN: (数字)9781728186955

ISBN: (纸本)9781728186962

As one of the most frequently launched applications in vehicular networks, live-streaming service is gaining momentum from the rapid development of automotive industry and telecommunication technologies. However, due to the dynamic characteristics of wireless links and topology of vehicular networks, providing high quality, low latency, and low variance video streaming for vehicles is a big challenge. This study aims to maximize video quality while decreasing time-delays and bitrate switches in vehicular fog computing (VFC) systems, by jointly optimizing vehicle scheduling, bitrate selection, computational and spectrum resource allocation. Considering the dynamic characteristics of vehicular networks and available computational/spectrum resource, the above problem is modeled as a Markov decision process (MDP). Since the action space of the MDP is multi-dimensional continuous variables mixed with discrete variables, the study utilizes an actor-critic deep reinforcement learning (DRL) algorithm to resolve the MDP problem. Extensive simulation results based on the dataset of the real world show the effectiveness of the proposed algorithm.

关键词： Streaming media Bit rate Resource management transcoding Computational modeling Servers Video recording

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CVT: A Crowdsourcing Video transcoding Scheme Based on Blockchain Smart Contracts

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IEEE ACCESS 2020年 8卷 220672-220681页

作者： Chen, Yuling Yin, Hongyan Xiang, Yuexin Ren, Wei Ren, Yi Xiong, Neal Naixue Guizhou Univ Coll Comp Sci & Technol Guiyang 550025 Peoples R China State Key Lab Publ Big Data Guiyang 550025 Peoples R China Tech Ctr Beijing Customs Beijing 100176 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Univ East Anglia Sch Comp Sci Norwich NR4 7TJ Norfolk England Northeastern State Univ Dept Math & Comp Sci Tahlequah OK 74464 USA

Streaming media has been largely used by millions of users every day. The number of customers and programs, e.g., TV series, movies, and various shows, are still growing fast. However, the demand for video transcoding for various personal terminal devices results in the shortage of computing resources and the prolongation of processing delay in centralized video transcoding systems. To solve this issue, we propose a blockchain, especially, smart contract based scheme that can achieve decentralized and on-demand crowdsourcing for video transcoding, which remarkably mitigates the transcoding overhead. Specifically, our scheme consists of four key components such as employers, workers, task allocation, and payment. An employer initializes the smart contract, releases the task, and initiates the smart contract. Workers bid for the task, and the successful bidder will obtain the task and execute the task. The task allocation mechanism and the payment mechanism can guarantee the profits of both and encourage both as well. Moreover, the smart contract consists of the bidding contract and the task execution contract. The extensive analysis of our proposed scheme justified the feasibility, security for defending against typical threats, applicability in realistic situations, and portability for most multimedia such as videos and audios.

关键词： Crowdsourcing transcoding Task analysis Streaming media Smart contracts Blockchain Resource management Blockchain crowdsourcing video transcoding smart contracts

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The transcoding of the Codex Xolotl in Don Fernando de Alva Ixtlilxochitl's Historia de la nacion chichimeca

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ETHNOHISTORY 2019年第1期66卷 71-94页

作者： Loaeza, Pablo Garcia West Virginia Univ Morgantown WV 26506 USA

This article considers the interplay among interpretation, intent, and ingenuity involved in don Fernando de Alva Ixtlilxochitl's transcoding of the Codex Xolotl in the first part of his definitive work, the Historia de la nacion chichimeca, by way of his earlier Sumaria relacion de todas las cosas de la Nueva Espana. Scholars tend to focus on Alva Ixtlilxochitl's role as a more or less reliable intermediary through whom the Codex Xolotl's information has been transmitted. However, in order to better understand his contribution to Mexican ethnohistory, it is important to acknowledge him as a diligent researcher who was also an exceptional writer and storyteller.

关键词： Codex Xolotl Fernando de Alva Ixtlilxochitl Historia de la nacion chichimeca transcoding

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MAMUT: Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-User Video transcoding 22

MAMUT: Multi-Agent Reinforcement Learning for Efficient Real...

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22nd Design, Automation and Test in Europe Conference and Exhibition (DATE)

作者： Costero, Luis Iranfar, Arman Zapater, Marina Igual, Francisco D. Olcoz, Katzalin Atienza, David Univ Complutense Madrid Dept Arquitectura Comp & Automat Madrid Spain Swiss Fed Inst Technol Lausanne EPFL Embedded Syst Lab Lausanne Switzerland

ISBN: (纸本)9783981926323

Real-time video transcoding has recently raised as a valid alternative to address the ever-increasing demand for video contents in servers' infrastructures in current multi-user environments. High Efficiency Video Coding (HEVC) makes efficient online transcoding feasible as it enhances user experience by providing the adequate video configuration, reduces pressure on the network, and minimizes inefficient and costly video storage. However, the computational complexity of HEVC, together with its myriad of configuration parameters, raises challenges for power management, throughput control, and Quality of Service (QoS) satisfaction. This is particularly challenging in multi-user environments where multiple users with different resolution demands and bandwidth constraints need to be served simultaneously. In this work, we present MAMUT, a multi-agent machine learning approach to tackle these challenges. Our proposal breaks the design space composed of run-time adaptation of the transcoder and system parameters into smaller sub-spaces that can be explored in a reasonable time by individual agents. While working cooperatively, each agent is in charge of learning and applying the optimal values for internal HEVC and system-wide parameters. In particular, MAMUT dynamically tunes Quantization Parameter, selects number of threads per video, and sets the operating frequency with throughput an d video quality objectives under compression and power consumption constraints. We implement MAMUT on an enterprise multicore server and compare equivalent scenarios to state-of-the-art alternative approaches. The obtained results reveal that MAMUT consistently attains up to 8x improvement in terms of FPS violations (and thus Quality of Service), 24% power reduction, as well as faster and more accurate adaptation both to the video contents and available resources.

关键词： Streaming media Real-time systems Throughput transcoding Bit rate Quality of service

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QoE Aware transcoding for Live Streaming in SDN-based Cloud-aided HetNets: An Actor-Critic Approach

QoE Aware Transcoding for Live Streaming in SDN-based Cloud-...

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IEEE International Conference on Communications (IEEE ICC)

作者： Zhang, Zhicai Wang, Ru Yu, F. Richard Fu, Fang Yan, Qiao Jiao, Qi Shanxi Univ Sch Phys & Elect Engn Taiyuan Shanxi Peoples R China Carleton Univ Dept Syst & Comp Engn Ottawa ON Canada Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Guangdong Peoples R China

ISBN: (纸本)9781728123738

With the advances in hand-held devices (smart phones and tablets, etc.) and high speed wireless networks, users have an explosive growth demand for live streaming service. Due to the diversity of user equipments (UEs), the live streaming has to be transcoded as different versions. However, transcoding is a computationally expensive and time consuming process. Since the shortage of computational resources and unstable of wireless networks, providing strict delay requirement and high quality live videos for wireless UEs is a big challenge. In this paper, we investigate user scheduling, transcoding decision, computational and wireless spectrum resources allocation problem in software defined networking (SDN) based cloud-aided of heterogeneous networks (HetNets). Our research focuses on improving UEs' quality of experience (QoE) while guaranteeing time-delay requirement for live streaming services. Different from existing literature, to approach the real wireless environment, the available computational and wireless spectrum resources are modeled as random processes in our research. Considering dynamic characteristics of wireless networks and the available resources, the above problem is modeled as a Markov decision problem (MDP). Since the action space of the MDP is multi-dimensional continuous variables mixed with discrete variables, traditional learning algorithms are powerless. Therefore, an online actor critic algorithm is proposed to resolve the problem. Simulation results show the proposed algorithm has superior performances compared with the policy gradient algorithm and deep Q learning network (DQN).

关键词： Wireless networks Activity spaces transcoding Streaming Markov chain wireless Stochastic Processes dynamic characteristics

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A New Video transcoding for Future Wireless Communication System 7

A New Video Transcoding for Future Wireless Communication Sy...

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7th International Conference on Electrical Engineering and Informatics (ICEEI)

作者： Ibrahim, Sarmad K. Khamiss, Nasser N. Mustansiriyah Univ Coll Engn Baghdad Iraq Al Nahrain Univ Coll Informat Engn Baghdad Iraq

ISBN: (纸本)9781728124186

Most of the information transmitted via the Internet is video information, where the rate of transmission of this data has increased and the variety of applications that use this type of information such as interactive video service and high-definition live broadcasting services, as there is congestion in the wireless network. This paper proposes a new video transcoding coding based on the data flow rate of the fifth and fourth generation communication systems. It compares a high-efficiency video system with the best parameters and the proposed transcoding system. The evaluation results are based on the rate of data consumption, video quality, and processing speed. The video transcoding system is the best in terms of bit rate saving and encoding time, the bit rate saving in the proposed coding about 13% as compared to the H265.

关键词： 4G 5G AVC HEVC transcoding

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Complexity Scalable HEVC-to-AV1 transcoding Based on Coding Tree Depth Inheritance 27

Complexity Scalable HEVC-to-AV1 Transcoding Based on Coding ...

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27th European Signal Processing Conference (EUSIPCO)

作者： Borges, Alex Zatt, Bruno Porto, Marcelo Agostini, Luciano Correa, Guilherme Fed Univ Pelotas UFPel Comp Sci Grad Program PPGC Video Technol Res Grp ViTech Pelotas RS Brazil

ISBN: (纸本)9789082797039

With the advent of the recently launched AOMedia Video 1 (AV1) bitstream specification, there is currently a need for converting legacy content encoded with the state-of-the-art High Efficiency Video Coding (HEVC) standard to the new format. However, transcoding is a complex task composed of a decoding and an encoding process in sequence, which requires long processing time and high energy consumption. This paper proposes a complexity scalable HEVC-to-AV1 transcoding solution with three operation modes, which allow adjusting the tradeoff between encoding efficiency and computational cost. The solution is based on the high correlation between block size decisions in HEVC and AV1, allowing the AV1 encoder to inherit Coding Tree depth information from the HEVC bitstream to constrain the AV1 re-encoding process. Experimental results for the three operation modes show a transcoding time reduction between 35.4% and 69.5% at the cost of a compression efficiency loss that varies between 4.9% and 16.8%.

关键词： transcoding HEVC AV1 video coding complexity reduction

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