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Adaptive Video Streaming With Edge Caching and Video transcoding Over Software-Defined Mobile Networks: A Deep Reinforcement Learning Approach

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2020年第3期19卷 1577-1592页

作者： Luo, Jia Yu, F. Richard Chen, Qianbin Tang, Lun Chongqing Univ Posts & Telecommun Chongqing Key Lab Mobile Commun Technol Chongqing 400065 Peoples R China Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada

Both mobile edge cloud (MEC) and software-defined networking (SDN) are technologies for next generation mobile networks. In this paper, we propose to simultaneously optimize energy consumption and quality of experience (QoE) metrics in video streaming over software-defined mobile networks (SDMN) combined with MEC. Specifically, we propose a novel mechanism to jointly consider buffer dynamics, video quality adaption, edge caching, video transcoding and transmission. First, we assume that the time-varying channel is a discrete-time Markov chain (DTMC). Then, based on this assumption, we formulate two optimization problems which can be depicted as a constrained Markov decision process (CMDP) and a Markov decision process (MDP). Then, we transform the CMDP problem into regular MDP by deploying Lyapunov technique. We utilize asynchronous advantage actor-critic (A3C) algorithm, one of the model-free deep reinforcement learning (DRL) methods, to solve the corresponding MDP issues. Simulation results are presented to show that the proposed scheme can achieve the goal of energy saving and QoE enhancement with the corresponding constraints satisfied.

关键词： Streaming media Quality of experience transcoding Adaptation models Bit rate Markov processes Cloud computing Software defined mobile networks mobile edge cloud adaptive video streaming Lyapunov technique deep reinforcement learning

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A Deep Reinforcement Learning-Based Transcoder Selection Framework for Blockchain-Enabled Wireless D2D transcoding

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IEEE TRANSACTIONS ON COMMUNICATIONS 2020年第6期68卷 3426-3439页

作者： Liu, Mengting Teng, Yinglei Yu, F. Richard Leung, Victor C. M. Song, Mei Beijing Univ Posts & Telecommun Beijing Key Lab Space Ground Interconnect & Conve Beijing 100876 Peoples R China Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada

The boom of video streaming industry has resulted in the increasing demands for transcoding services from heterogeneous users. Recent advances of blockchain technology allow some startups to realize decentralized collaborative transcoding through device-to-device (D2D) networks, where a group of transcoders are selected to perform transcoding cooperatively. For the blockchain-enabled D2D transcoding systems, it's imperative to jointly design transcoder selection, task scheduling and resource allocation schemes in order to provide efficient and trustworthy transcoding services. In this paper, viewing the involved multi-dimensional complex factors and channel fluctuation, we propose a novel deep reinforcement learning (DRL) based transcoder selection framework for blockchain enabled D2D transcoding systems where both the platform dynamics and channel statistics are captured. To reduce the action space size, we adopt a two-stage decision approach to first select the transcoders through a normal DRL based framework and then obtain the optimal task scheduling, power control, and resource allocation scheme by solving a stochastic optimization problem with the constrained stochastic successive convex approximation (CSSCA) approach. Simulation results show that our proposed framework can achieve high transcoding revenue while meeting the quality of service (QoS) requirements, and it can well handle dynamic cases.

关键词： transcoding Device-to-device communication Resource management Task analysis Quality of service Streaming media Blockchain deep reinforcement learning (DRL) transcoder selection

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A Mobile Edge Computing (MEC)-Enabled transcoding Framework for Blockchain-Based Video Streaming

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IEEE WIRELESS COMMUNICATIONS 2020年第2期27卷 81-87页

作者： Liu, Mengting Teng, Yinglei Yu, F. Richard Leung, Victor C. M. Song, Mei Beijing Univ Posts & Telecommun Beijing Key Lab Work Safety Intelligent Monitorin Beijing Peoples R China Carleton Univ Ottawa ON Canada Univ British Columbia Elect & Comp Engn Vancouver BC Canada Univ British Columbia TELUS Mobil Res Chair Vancouver BC Canada

Blockchain technology is reshaping the video streaming industry by changing how humans create, distribute and consume videos. Leveraging blockchain technology, some emerging video streaming platforms aim to eliminate the centralized video streaming servers by building peer-to-peer content distribution architectures and cryptocurrency-based payment systems. On these blockchain-based platforms, the source video streams are required to be transcoded into multiple versions to meet heterogeneous user demands, which is a computationally intensive and energy consuming process. Meanwhile, the block size of the blockchain has significant impacts on the performance of blockchain-based video streaming systems. In this article, we propose a novel MEC-enabled transcoding framework for blockchain-based video streaming while an adaptive block size scheme is designed for the underlying blockchains. The system architecture of the proposed framework is presented to enable coordination of different aspects. Then we address the video transcoding and block size issues by using the ADMM method, and smart contract is used to facilitate distributed optimization among non-trusting entities. Simulation results are presented to show the effectiveness of the proposed framework.

关键词： Streaming media transcoding Blockchain Peer-to-peer computing Smart contracts Cryptography Optimization

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Hardware-Assisted, Low-Cost Video transcoding Solution in Wireless Networks

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2020年第3期19卷 581-597页

作者： Yoon, Jongwon Banerjee, Suman Hanyang Univ Coll Comp Ansan 15588 South Korea Univ Wisconsin Dept Comp Sci 1210 W Dayton St Madison WI 53706 USA

Wireless video streaming has become an extremely popular application in recent years. Internet video streaming to mobile devices, however, faces several challenges, e.g., unstable wireless connections, long latency, high jitter and etc. Bitrate adaptive streaming and video transcoding solutions are widely used to address the above-mentioned issues, however, there are still several shortcomings of these approaches. Such challenges hinder providing satisfactory quality of video streaming service to the mobile users. We propose a hardware-assisted, real-time video transcoding solution implemented on a commercial off-the-shelf device, Raspberry Pi. We employ the software and hardware coupled architecture in order to improve the performance/quality of video streaming and enhance the user satisfactions in wireless network. Our video transcoding solution can be applied to both the downlink and uplink streaming: for downlink stream, it can provide agile bitrate adaptation to sudden network dynamics and enhance video quality by running our transcoding solution at the wireless edge. It can be used to uplink stream for broadcasting live streams in real-time. We present the design and implementation of our video transcoding system in both cases with practical scenarios. The evaluation results reveal that our transcoding solution enhances the performance of video streaming compared with other adaptive bitrate streamings and it provides higher video quality without causing rebuffering or video stall. We bridge the gap between the wireless channel capacity and the video quality while providing a better streaming experience to end user.

关键词： Streaming media transcoding Bit rate Media Servers Wireless communication Real-time systems Adaptive bitrate streaming video transcoding wireless networks

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Video Caching and transcoding in Wireless Cellular Networks With Mobile Edge Computing: A Robust Approach

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2020年第8期69卷 9234-9238页

作者： Wang, Chenmeng Feng, Daquan Zhang, Shengli Chen, Qianbin Shenzhen Univ Guangdong Prov Engn Lab Digital Creat Technol Shenzhen 518060 Peoples R China Chongqing Univ Posts & Telecomm CQUPT Chongqing 400065 Peoples R China

In this correspondence, we jointly optimize the caching decision and radio and computation resource allocation for video service in mobile edge computing (MEC) enabled network to maximize the system revenue. To tolerate the uncertainty in the network traffic and avoid "hard constraint" based on constant content request rate, we employ robust optimization to obtain optimal caching decisions, and then accordingly allocate the radio and computation resources for video transcoding. Simulation results demonstrate the effectiveness of the proposed scheme.

关键词： Optimization Streaming media transcoding Servers Artificial neural networks Edge computing Resource management Mobile edge computing small cell networks computation offloading resource allocation in-network caching

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Caching and Recommendation Decisions at transcoding-Enabled Base Stations

Caching and Recommendation Decisions at Transcoding-Enabled ...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Dimitra Tsigkari Thrasyvoulos Spyropoulos EURECOM Biot France Technical University of Crete Chania Greece

ISBN: (纸本)9781665435413

In the context of on-demand video streaming services, both the caching and the recommendation decisions have an impact on the user satisfaction, and thus, financial implications for the Content Provider (CP). The idea of co-designing these decisions has been recently proposed in the literature as a way to minimize delivery costs and traffic at the backbone Internet. However, related work does not take into account that every content exists in multiple versions/streaming qualities, or at best treats each version as a separate content, when it comes to caching. In this paper, we explore how transcoding a content at the edge could avoid placing multiple related versions of this content in the same cache, thus better utilizing capacity (leading to an increase of the CP's profit). To this end, we formulate the problem of jointly deciding on caching, recommendations, and user-transcoder assignments with the goal of increasing the profit (revenue minus the incurred costs). We propose an iterative algorithm that is based on a decomposition of the formulated problem into two subproblems. We show that both subproblems, although NP-hard, are equivalent to problems in the literature for which algorithms with approximation guarantees exist. Our numerical evaluations in realistic scenarios show that the proposed policy leads to important financial gains of up to 29% when compared to the scenario where edge transcoding is not exploited.

关键词： Base stations Costs transcoding Streaming media Approximation algorithms Internet Iterative methods

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Fast Rules to Speedup HEVC to VVC transcoding 2021

Fast Rules to Speedup HEVC to VVC Transcoding

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25th Pan-Hellenic Conference on Informatics

作者： Natalia Panagou Panagiotis Belememis Panos K. Papadopoulos Maria Koziri Thanasis Loukopoulos University of Thessaly Greece

ISBN: (纸本)9781450395557

With the advent of the newly introduced Versatile Video Coding Standard (VVC) the need for converting legacy content from its widely used predecessor, High Efficiency Video Coding (HEVC), to the new format has emerged due to the higher compression performance of the new codec. The most straightforward transcoding solution is to fully decode and re-encode the incoming video signal to the new coding format, in other respects a notably time-consuming operation. In this paper, we propose a fast HEVC to VVC transcoding algorithm that exploits the high correlation between block partitioning decisions in HEVC and VVC in order to early terminate the block partitioning process in VVC and consequently reduce the transcoding time complexity.

关键词： hevc transcoding video coding vvc complexity reduction

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Cost-efficient HEVC-based quadtree splitting (HEQUS) for VVC Video transcoding

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SIGNAL PROCESSING-IMAGE COMMUNICATION 2021年 94卷 116199-116199页

作者： Garcia-Lucas, D. Cebrian-Marquez, G. Diaz-Honrubia, A. J. Mallikarachchi, T. Cuenca, P. Univ Castilla La Mancha High Performance Networks & Architectures Albacete Spain Univ Politecn Madrid ETS Ingenieros Informat Madrid Spain Cardiff Metropolitan Univ Sch Technol Cardiff Wales

The release of the latest video coding standard, known as Versatile Video Coding (VVC), has created the need to convert current High Efficiency Video Coding (HEVC) content to this new standard. However, the traditional cascade transcoding pipeline is not effective due to the exorbitant computational complexity of VVC. With this in mind, this paper proposes a fast HEVC-VVC transcoder that implements a probabilistic classifier based on Na?ve-Bayes at the first partitioning level (128 ? 128 pixels). This model uses features extracted from the 128 ? 128 size blocks of the residual and reconstructed frames in the HEVC bitstream, and their correlation with the block partitioning structure. For the subsequent VVC coding depth levels, partitioning decisions are derived from the HEVC structure. The results achieve a 57.08% transcoding time reduction with a BD-rate penalty of 2.40%, compared with a traditional transcoding approach for the random access encoding configuration.

关键词： HEVC H 265 VVC transcoding MTT

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Learned Lossless JPEG transcoding via Joint Lossy and Residual Compression

Learned Lossless JPEG Transcoding via Joint Lossy and Residu...

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IEEE Visual Communications and Image Processing (VCIP)

作者： Xiaoshuai Fan Xin Li Zhibo Chen Unviersity of Science and Technology of China Hefei China

ISBN: (纸本)9781665475938

As a commonly-used image compression format, JPEG has been broadly applied in the transmission and storage of images. To further reduce the compression cost while maintaining the quality of JPEG images, lossless transcoding technology has been proposed to recompress the compressed JPEG image in the DCT domain. Previous works, on the other hand, typically reduce the redundancy of DCT coefficients and optimize the probability prediction of entropy coding in a hand-crafted manner that lacks generalization ability and flexibility. To tackle the above challenge, we propose the learned lossless JPEG transcoding framework via Joint Lossy and Residual Compression. Instead of directly optimizing the entropy estimation, we focus on the redundancy that exists in the DCT coefficients. To the best of our knowledge, we are the first to utilize the learned end-to-end lossy transform coding to reduce the redundancy of DCT coefficients in a compact representational domain. We also introduce residual compression for lossless transcoding, which adaptively learns the distribution of residual DCT coefficients before compressing them using context-based entropy coding. Our proposed transcoding architecture shows significant superiority in the compression of JPEG images thanks to the collaboration of learned lossy transform coding and residual entropy coding. Extensive experi-ments on multiple datasets have demonstrated that our proposed framework can achieve about 21.49% bits saving in average based on JPEG compression, which outperforms the typical lossless transcoding framework JPEG-XL by 3.51%.

关键词： Image coding Visual communication Redundancy Transform coding Termination of employment transcoding Entropy coding

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Allies: Tile-Based Joint transcoding, Delivery and Caching of 360° Videos in Edge Cloud Networks 13

Allies: Tile-Based Joint Transcoding, Delivery and Caching o...

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IEEE 13th International Conference on Cloud Computing (CLOUD)

作者： Shi, Jianxin Pu, Lingjun Xu, Jingdong Nankai Univ Coll Comp Sci Inst Syst & Networks Tianjin Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China Tianjin Key Lab Network & Data Secur Technol Tianjin Peoples R China

ISBN: (纸本)9781728187808

360 degrees or panoramic video applications have seen booming development and absorbed great attention in recent years. However, as they are of significant size and usually watched from a close distance, they require an extremely higher bandwidth and frame rate for a good immersible experience, which poses a great challenge on mobile networks. Realizing the great potentials of tile-based transcoding, viewport adaptive streaming, and edge caching, we propose Allies, a tile-based joint transcoding, delivery, and caching framework for 360 degrees video services in edge cloud networks. Meanwhile, an innovative idea about 360 degrees video caching way is proposed and applied to improve the cache utilization of edge clouds. In this framework, we formulate the joint optimization problem as an integer nonlinear program and propose a greedy suboptimal algorithm with polynomial running time to minimize video service costs. Finally, extensive simulations with real user's head movement traces and corresponding 360 degrees video datasets corroborate the efficiency, flexibility, and lightweight of our proposed algorithm;for instance, it achieves over 25% performance improvement compared to state-of-the-art works in various system settings.

关键词： transcoding viewport adaptive streaming caching request scheduling edge cloud

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