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Networking Systems of AI (INSAI), International Conference on

作者： Chi Xu Zhengzhe Li Guo Qing Huai Jia Zhao Yifei Zhu Xiaoqiang Ma Haiyang Wang Simon Fraser University New York University University of Toronto Shanghai Jiao Tong University Douglas College University of Minnesota at Duluth

ISBN: (数字)9798350344660

ISBN: (纸本)9798350344677

Smart buildings integrate users' digitized wearables with their physical surroundings, creating a seamless and interactive user experience. This is achieved through the utilization of multiple sensors, video streaming, artificial intelligence, and edge computing. These technologies gather extensive data and offer users a wide range of applications, such as 3D audio/video in AR/VR, localization, virtual tours, and vigilant monitoring. However, modern AR/VR devices face limitations due to the bulkiness and discomfort of various on-body sensing hardware, like headsets and specialized glasses. These components often prove unsuitable for extended use. Developing an immersive system that combines lightweight interaction with high-quality presentation remains a significant challenge. This paper introduces a comprehensive system for lightweight immersive interaction tailored specifically for smart buildings. The system consists of the following components: 1). A lightweight panoramic imaging framework to address the challenges related to hardware size and functionality. 2). A learning-based video transcoding cost prediction framework for efficient load balancing. 3). A layered networking architecture designed to facilitate high-quality mobile live streaming. Our experimental results demonstrate the effectiveness of the system design, showcasing its seamless operation across different times, geographical locations, and heterogeneous wireless networks. It delivers a large-scale, high-quality experience while supporting low-latency interactive applications.

关键词： Smart buildings Three-dimensional displays Wireless networks transcoding Hardware User experience Wearable devices Smart devices System analysis and design Monitoring

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Accelerating the CU partitioning decision in an HEVC-JEM transcoder

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第3-4期79卷 2047-2067页

作者： Garcia-Lucas, D. Cebrian-Marquez, G. Diaz-Honrubia, A. J. Cuenca, Pedro Univ Castilla La Mancha High Performance Networks & Architectures Lab Albacete Spain Univ Oviedo Comp Sci Dept Oviedo Spain Univ Politecn Madrid ETS Ingn Informat Madrid Spain

High Efficiency Video Coding (HEVC) is currently the latest video coding standard available on the market, and it is able to offer up to twice the coding efficiency, in the range of 50% bitrate reduction for the same video quality, of the previous standard, namely H.264/Advanced Video Coding (AVC). HEVC was standardized in 2013 for videos up to a resolution of 2K. However, the popularity of 4K videos is increasing due to the growing use of video-on-demand platforms. Therefore, the ITU-T Video Coding Expert Group (VCEG) and the ISO/IEC Moving Picture Expert Group (MPEG) created the Joint Video Exploration Team (JVET) in 2015 to design the future video coding technology under the Joint Exploration Model (JEM), which its latest version achieves an improvement in coding efficiency of 30%, but at a high cost in terms of computational complexity (10x) with respect to HEVC. The new video standard is expected to be ready in 2020, so it is necessary to find efficient mechanisms to convert current content to the new format adopted in JEM. In this regard, our proposal consists in a probabilistic classifier based on Naive-Bayes that enables the prediction of the splitting decision at the first quadtree level in JEM, reducing the computational complexity of the transcoding process from HEVC to this new standard. The experimental results show a good trade-off between coding efficiency and complexity compared with the anchor transcoder, obtaining a time reduction up to 12.71% at the expense of low coding efficiency penalties in the configurations evaluated.

关键词： HEVC H 265 JEM transcoding CTU splitting

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Tamper-Proofing Video With Hierarchical Attention Autoencoder Hashing on Blockchain

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IEEE TRANSACTIONS ON MULTIMEDIA 2020年第11期22卷 2858-2872页

作者： Bui, Tu Cooper, Daniel Collomosse, John Bell, Mark Green, Alex Sheridan, John Higgins, Jez Das, Arindra Keller, Jared Robert Thereaux, Olivier Univ Surrey Ctr Vis Speech & Signal Proc CVSSP Guildford GU2 7XH Surrey England Univ Surrey Guildford GU2 7XH Surrey England Adobe Res San Jose CA 95110 USA Natl Arch Richmond TW9 4DU Surrey England Open Data Inst London EC2A 4JE England

We present ARCHANGEL;a novel distributed ledger based system for assuring the long-term integrity of digital video archives. First, we introduce a novel deep network architecture using a hierarchical attention autoencoder (HAAE) to compute temporal content hashes (TCHs) from minutes or hour-long audio-visual streams. Our TCHs are sensitive to accidental or malicious content modification (tampering). The focus of our self-supervised HAAE is to guard against content modification such as frame truncation or corruption but ensure invariance against format shift (i.e. codec change). This is necessary due to the curatorial requirement for archives to format shift video over time to ensure future accessibility. Second, we describe how the TCHs (and the models used to derive them) are secured via a proof-of-authority blockchain distributed across multiple independent archives. We report on the efficacy of ARCHANGEL within the context of a trial deployment in which the national government archives of the United Kingdom, United States of America, Estonia, Australia and Norway participated.

关键词： Blockchain Feature extraction Visualization Distributed ledger Task analysis transcoding Streaming media Distributed Ledger Technology content aware hashing autoencoder LSTM attention network content integrity blockchain

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CDN Slicing over a Multi-Domain Edge Cloud

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2020年第9期19卷 2010-2027页

作者： Taleb, Tarik Frangoudis, Pantelis A. Benkacem, Ilias Ksentini, Adlen Aalto Univ Sch Elect Engn Dept Commun & Networking Espoo 02150 Finland Oulu Univ Oulu 90014 Finland Sejong Univ Seoul 143747 South Korea EURECOM Commun Syst Dept F-06410 Sophia Antipolis France

We present an architecture for the provision of video Content Delivery Network (CDN) functionality as a service over a multi-domain cloud. We introduce the concept of a CDN slice, that is, a CDN service instance which is created upon a content provider's request, is autonomously managed, and spans multiple, potentially heterogeneous, edge cloud infrastructures. Our design is tailored to a 5G mobile network context, building on its inherent programmability, management flexibility, and the availability of cloud resources at the mobile edge level, thus close to end users. We exploit Network Functions Virtualization (NFV) and Multi-access Edge Computing (MEC) technologies, proposing a system which is aligned with the recent NFV and MEC standards. To deliver a Quality-of-Experience (QoE) optimized video service, we derive empirical models of video QoE as a function of service workload, which, coupled with multi-level service monitoring, drive our slice resource allocation and elastic management mechanisms. These management schemes feature autonomic compute resource scaling, and on-the-fly transcoding to adapt video bit-rate to the current network conditions. Their effectiveness is demonstrated via testbed experiments.

关键词： Cloud computing Streaming media 5G mobile communication Resource management transcoding Computer architecture Network function virtualization Content delivery network (CDN) multi-access edge computing (MEC) network slicing management and orchestration (MANO) network functions virtualization (NFV) 5G mobile network

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DeepCast: Towards Personalized QoE for Edge-Assisted Crowdcast With Deep Reinforcement Learning

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IEEE-ACM TRANSACTIONS ON NETWORKING 2020年第3期28卷 1255-1268页

作者： Wang, Fangxin Zhang, Cong Wang, Feng Liu, Jiangchuan Zhu, Yifei Pang, Haitian Sun, Lifeng Simon Fraser Univ Sch Comp Sci Burnaby BC V5A 1S6 Canada Univ Sci & Technol China Sch Comp Sci Hefei 230052 Peoples R China Univ Mississippi Dept Comp & Informat Sci Oxford MS 38677 USA Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China

Today's anywhere and anytime broadband connection and audio/video capture have boosted the deployment of crowdsourced livecast services (or crowdcast). Bridging a massive amount of geo-distributed broadcasters and their fellow viewers, such representatives as ***, Youtube Gaming, and ***, have greatly changed the generation and distribution landscape of streaming content. They also enable rich online interactions among the crowd, and strive to offer personalized Quality-of-Experience (QoE) for individual viewers. Given the ultra-large scale and the dynamics of the crowd, personalizing QoE however is much more challenging than in early generation streaming services. The rich interactions among the broadcasters, viewers, and the network system, on the other hand, also offer invaluable data that could be utilized towards informed management. This paper presents DeepCast, an edge-assisted crowdcast framework that explores the sheer amount of viewing data towards intelligent decisions for personalized QoE demands. DeepCast seamlessly integrates cloud, CDN, and edge servers for crowdcast content distribution, and advocates a data-driven design that extracts the hidden information from the complex interactions among the system components. Through deep reinforcement learning (DRL), it automatically identifies the most suitable strategies for viewer assignment and transcoding at edges. We collect multiple real-world datasets and evaluate the performance of DeepCast with trace-driven experiments. The results demonstrate its flexibility and effectiveness towards better personalized QoE and lower cost for crowdcast systems.

关键词： Quality of experience Streaming media Bit rate transcoding Servers Switches Games Crowdsourced livecast edge computing personalized QoE deep reinforcement learning

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Energy-Efficient Proactive Caching for Adaptive Video Streaming via Data-Driven Optimization

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IEEE INTERNET OF THINGS JOURNAL 2020年第6期7卷 5549-5561页

作者： Li, Liang Shi, Dian Hou, Ronghui Chen, Rui Lin, Bin Pan, Miao Xidian Univ Sch Cyber Engn Xian 710071 Peoples R China Univ Houston Dept Elect & Comp Engn Houston TX 77204 USA Dalian Maritime Univ Coll Informat Sci & Technol Dalian 116026 Peoples R China

Proactive caching in mobile-edge computing (MEC) networks is promising to handle the ever-increasing demand for wireless video services, and transcoding at MEC servers further improves the flexibility of video content delivery. However, how to effectively conduct caching for adaptive bitrate streaming poses great challenges due to the uncertainty of user preferences. The caching decisions also have a profound impact on the system energy efficiency since they may change the video delivery modes. In this article, by integrating caching, transcoding, and backhaul retrieving in a MEC-enabled adaptive streaming system, we propose a holistic solution to jointly determine the caching of bitrate-aware files and the scheduling of video requests in an energy-efficient manner. Specifically, we leverage a data-driven approach to characterize the uncertainty of real request arrivals. Based on the uncertainty model, we formulate a data-driven risk-averse optimization to derive a robust strategy for caching and delivery scheduling, which is a two-stage stochastic mixed-integer programming (SMIP) with the goal of minimizing the total expected energy consumption. We also develop feasible solutions and conduct extensive simulations on real-world data sets. The results validate the effectiveness of the proposed scheme in both the energy efficiency and the cache hit ratio.

关键词： Streaming media Optimization Bit rate Adaptive systems transcoding Servers Uncertainty Adaptive video streaming data-driven optimization mobile-edge computing (MEC) proactive caching

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Fast H.264/AVC to HEVC transcoding based on Machine Learning

Fast H.264/AVC to HEVC Transcoding based on Machine Learning

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International Telecommunications Symposium (ITS)

作者： Peixoto, Eduardo Macchiavello, Bruno de Queiroz, Ricardo. L. Hung, Edson Mintsu Univ Brasilia Dept Elect Engn Campus Univ Darcy Ribeiro BR-70910900 Brasilia DF Brazil Univ Brasilia Dept Ciencia Comp BR-70910900 Brasilia DF Brazil Univ Brasilia Fac UnB Gama BR-70910900 Brasilia DF Brazil

ISBN: (纸本)9781479937431

Since the HEVC codec has become an ITU-T and ISO/IEC standard, efficient transcoding from previous standards, such as the H.264/AVC, to HEVC is highly needed. In this paper, we build on our previous work with the goal to develop a faster transcoder from H.264/AVC to HEVC. The transcoder is built around an established two-stage transcoding. In the first stage, called the training stage, full re-encoding is performed while the H.264/AVC and the HEVC information are gathered. This information is then used to build a CU classification model that is used in the second stage (called the transcoding stage). The solution is tested with well-known video sequences and evaluated in terms of rate-distortion and complexity. The proposed method is 3.4 times faster, on average, than the trivial transcoder, and 1.65 times faster than a previous transcoding solution.

关键词： Rate-distortion Standards Streaming media Training transcoding Video coding

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Energy Efficiency and Delay Optimization for Edge Caching Aided Video Streaming

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2020年第11期69卷 14116-14121页

作者： Zhou, Guorong Zhao, Liqiang Wang, Yunfeng Zheng, Gan Hanzo, Lajos Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Loughborough Univ Wolfson Sch Mech Elect & Mfg Engn Loughborough LE11 3TU Leics England Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ Hants England

In this paper, we design a computing, communication and caching scheme for edge caching-based video streaming in order to improve the network performance. Firstly, we optimize the system's energy efficiency and delay with the aid of network function virtualization. Then, a dynamic edge caching decision is developed, and based on Lyapunov optimization, an alternating resource optimization algorithm is proposed for allocating the optimal subcarrier and power resources, video caching and computing resources. Our numerical results show that the proposed scheme outperforms both the traditional caching scheme as well as the least frequently used (LFU)-40% regime, and strikes a compelling tradeoff between the energy efficiency and delay.

关键词： Streaming media Delays Optimization Resource management transcoding Computational modeling Network function virtualization Edge caching video streaming computing energy efficiency delay

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Local Search Heuristics for Media Streams Planning with transcoding 13

Local Search Heuristics for Media Streams Planning with Tran...

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13th IEEE International Symposium on Network Computing and Applications (NCA)

作者： Marek, Jiri Rudova, Hana Holub, Petr Masaryk Univ Fac Informat Brno Czech Republic Masaryk Univ Inst Comp Sci Brno Czech Republic

ISBN: (纸本)9781479953936

Recent advances in processing capabilities of commodity hardware enabled deployment of high-performance collaborative environments in various application fields, where maximum image quality and interactivity are critical. In this paper, we focus on user-empowered collaborative environments that rely on information available to the users and that can be obtained from the end-nodes connected to the network. We build upon our previous work on scheduling strategies for multi-point data distribution, while utilizing media transcoding during the distribution and partial network topology knowledge that may be provided by the users. The novelty introduced by this paper focuses on improving scheduling scalability by employing local search metaheuristics, allowing us to maintain systems comprising more than twice as many applications compared to the previous approaches.

关键词： Collaboration Knowledgeengineering Media Networktopology Planning Topology transcoding CoUniverse Localsearch MediaStreamsPlanningProblem RouletteWheelSelection UltraGrid multimediatransfers partialtopologyknowledge real-timeplanning transcoding transcoding media streaming topology Planning

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MEC-enabled video streaming in device-to-device networks

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IET COMMUNICATIONS 2020年第15期14卷 2453-2461页

作者： Zhang, Xuguang Lin, Huangda Chen, Mingkai Kang, Bin Wang, Lei Nanjing Univ Posts & Telecommun Natl Engn Res Ctr Commun & Network Technol Nanjing Peoples R China Nanjing Univ Posts & Telecommun Key Lab Broadband Wireless Commun & Sensor Networ Minist Educ Nanjing Peoples R China

By offloading video streaming from the centralised cloud to the edge, mobile edge computing (MEC) servers offer new opportunities for real-time video transmission. Deploying on the edge of users can ensure low latency transmission, however, the limited storage and computing ability cannot adapt to the currently used video transmission technologies such as video transcoding or simulcast. To solve this problem, a more flexible video transmission architecture needs to be considered. Under this motivation, the authors propose a device-to-device (D2D) assisted video streaming scheme, which fuses the technical advantages of MEC and scalable video coding. Specifically, they first construct a novel architecture for delay-sensitive live video streaming services in edge-enabled wireless heterogeneous networks named MEC-enabled goodput-aware (MEGA) model. Then they present a mathematical formulation for optimising the aggregation goodput performance of video traffic including both cellular and D2D links. Finally, they derive a three-step solution based on a distributed heuristic algorithm. Numerical simulation results show that MEGA outperforms existing models in terms of goodput, end-to-end delay, effective loss rate, and users' quality-of-experience.

关键词： video streaming delays transcoding telecommunication traffic media streaming video coding MEC-enabled video device-to-device networks offloading video centralised cloud mobile edge computing servers real-time video transmission latency transmission computing ability currently used video transmission technologies video transcoding flexible video transmission architecture device-to-device assisted video streaming scheme scalable video coding delay-sensitive live video streaming services edge-enabled wireless heterogeneous networks MEC-enabled goodput-aware model video traffic

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