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IEEE International Symposium on Broadband multimedia systems and Broadcasting (BMSB)

作者： Roberto Viola Danilo Amendola Zaloa Ferná ndez Á lvaro Gabilondo Mikel Zorrilla Pablo Angueira Mikel Casals Jon Montalbá n Vicomtech Foundation Basque Research and Technology Alliance San Sebasti&#x00E1 n Spain Department of Communications Engineering University of the Basque Country Bilbao Spain Tecnun Universidad de Navarra San Sebasti&#x00E1 Department of Electronic Technology University of the Basque Country Bilbao Spain

Nowadays, video streaming services are the major sources of Internet traffic, and their usage is constantly growing. To cope with this increasing video traffic demand, 5G comes with a new Radio access Network (RAN), having increased network data rates, and the Multi-access Edge Computing (MEC) architecture, enabling the deployment of services closer to the end devices. 5G RAN and MEC open to new opportunities to enforce and boost the video content delivery, as the content can be cached locally at the MEC host and served with improved RAN connectivity. However, the presence of a cache located at MEC may affect player adaptation algorithm when employing Adaptive Bitrate (ABR) streaming technologies, such as Dynamic Adaptive Streaming over HTTP (DASH). The cache influences the network measurements performed by the DASH player, which are later used, among other factors, by its adaptation algorithm to select the video representation bitrate. This paper investigates the interactions between MEC cache and DASH adaptation algorithms in a real testbed, including 5G RAN and MEC platform. The effects on different adaptation algorithms are analyzed while caching the DASH video content at the MEC.

关键词： Multi-access edge computing 5G mobile communication Heuristic algorithms Bit rate Quality of service Computer architecture Streaming media

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Deep Learning-Driven Video Summarization on the Cloud: A Pathway to Efficient Storage and Quick access

Deep Learning-Driven Video Summarization on the Cloud: A Pat...

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IEEE International Symposium on Embedded Multicore Socs (MCSoC)

作者： Mahmoud Darwich Kasem Khalil Yasser Ismail Magdy Bayoumi Department of Mathematics and Computer Science University of Mount Union Alliance OH Department of Electrical and Computer Engineering University of Mississippi University MS Electrical Engineering Department Southern University and A&M College Baton Rouge LA Department of Electrical and Computer Engineering University of Louisiana at Lafayette LA

Cloud-based video summarization is an increasingly popular technique for managing large video repositories more efficiently. In this research, we present a novel approach for cloud-based video summarization that focuses on reducing storage costs and improving accessibility by automatically extracting critical information from videos and generating brief summaries. Our approach leverages cloud computing technology and deep learning algorithms for video analysis and processing. We evaluate the effectiveness of our approach using a real-world dataset of video files and demonstrate that it can generate high-quality video summaries while significantly reducing storage requirements. Our proposed approach has potential applications in areas such as security monitoring, video surveillance, and multimedia content management. This paper contributes to the existing literature by presenting a comprehensive study of cloud-based video summarization and proposing a novel solution that addresses the limitations of existing approaches, thereby improving the efficiency and cost-effectiveness of video management.

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Scalable Coding of 360-degree Video for Streaming Adaptation at 5G Network Edges 15

Scalable Coding of 360-degree Video for Streaming Adaptation...

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15th IEEE International Symposium on Broadband multimedia systems and Broadcasting (BMSB)

作者： Carreira, J. de Faria, Sergio M. M. Tavora, Luis M. N. Navarro, Antonio Assuncao, Pedro A. A. Inst Telecomunicacoes Lisbon Portugal Polytech Leiria ESTG Leiria Portugal Univ Aveiro Aveiro Portugal

ISBN: (纸本)9781728157849

YY The huge amount of data that is necessary to capture the full field-of-view (FoV) in omnidirectional video, i.e., 360 degrees, imposes the use of highly efficient compressed formats as well as adaptive broadcast and streaming mechanisms, such as those foreseen for 5G networks. To cope with the demanding requirements of 360 degrees video streaming over 5G networks, this work proposes a scalable 360 degrees video coding architecture, by enabling adaptation through the Multi-access Edge Computing (MEC) server in two different domains of the spherical visual content, namely spatial resolution and FoV. In the proposed architecture two-layers are encoded from the input 360 degrees video content: (i) the base-layer (BL), encoding each 360 degrees image as a whole, at a lower spatial resolution;(ii) the enhancement-layer (EL) encoding each spherical image as a set of multiple FoVs with higher spatial resolution. Such arrangement enables flexible stream adaptation for the smart decision algorithms to be implemented at the MEC server, enabling significant reduction of the overall bit rate through the radio interface. The simulation results show that the proposed scalable coding scheme allows a great deal of bit rate savings across the 5G network, achieving 36% of bit rate saving, on average, for a 90 degrees FoV in comparison with conventional single-layer coding.

关键词： Scalable video coding & content adaptation Future broadcasting services

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RL-Belady: A Unified Learning Framework for content Caching 20

RL-Belady: A Unified Learning Framework for Content Caching

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28th ACM International Conference on multimedia (MM)

作者： Yan, Gang Li, Jian SUNY Binghamton Binghamton NY 13902 USA

ISBN: (纸本)9781450379885

content streaming is the dominant application in today's Internet, which is typically distributed via content delivery networks (CDNs). CDNs usually use caching as a means to reduce user access latency so as to enable faster content downloads. Typical analysis of caching systems either focuses on content admission, which decides whether to cache a content, or content eviction to decide which content to evict when the cache is full. This paper instead proposes a novel framework that can simultaneously learn both content admission and content eviction for caching in CDNs. To attain this goal, we first put forward a lightweight architecture for content next request time prediction. We then leverage reinforcement learning (RL) along with the prediction to learn the time-varying content popularities for content admission, and develop a simple threshold-based model for content eviction. We call this new algorithm RL-Belady (RLB). In addition, we address several key challenges to design learning-based caching algorithms, including how to guarantee lightweight training and prediction with both content eviction and admission in consideration, limit memory overhead, reduce randomness and improve robustness in RL stochastic optimization. Our evaluation results using 3 production CDN datasets show that RLB can consistently outperform state-of-the-art methods with dramatically reduced running time and modest overhead.

关键词： Cache Admission Cache Eviction content Delivery Networks Machine Learning Belady

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Dual watermarking framework for privacy protection and content authentication of multimedia

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FUTURE GENERATION COMPUTER systems-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 94卷 654-673页

作者： Hurrah, Nasir N. Parah, Shabir A. Loan, Nazir A. Sheikh, Javaid A. Elhoseny, Mohammad Muhammad, Khan Univ Kashmir Post Grad Dept Elect & Instrumentat Technol Srinagar 190006 Jammu & Kashmir India Mansoura Univ Fac Comp & Informat El Gomhouria St Mansoura 35516 Egypt Sejong Univ Digital Contents Res Inst Intelligent Media Lab Seoul South Korea

In the current multimedia networked infrastructure, privacy breaches due to cyber-attacks result in huge economic losses. Despite these threats there is ever increasing demand to share data over various insecure networks for accomplishment of numerous tasks. In such a scenario there is a greater need to develop new algorithms for strengthening the existing cybersecurity frameworks, ensure security, privacy, copyright protection and authentication of data. In this paper a new technique for copyright protection, data security and content authentication of multimedia images is presented. The copyright protection of the media is taken care of by embedding a robust watermark using an efficient inter-block coefficient differencing algorithm and is proposed as Scheme I. Scheme II has been utilized to ensure both copyright protection, and content authentication. The authentication of the content has been ensured by embedding a fragile watermark in spatial domain while as copyright protection has been taken care of utilizing a robust watermark. In order to thwart an adversary and ensure that it has no access to actual embedded data, we make use of a novel encryption algorithm in conjunction with Arnold transform to encrypt data prior to its embedding. The experimental results reveal that the proposed framework offers high degree of robustness against single/dual/triple attacks;with Normalized Correlation (NCC), more than or equal to 0.95. Besides, the fragile watermark embedding makes the system capable of tamper detection and localization with average BER more than 45% for all signal processing/geometric attacks. The average PSNR achieved for both schemes is greater than 41 dB. A comparison of the proposed framework with various state-of-the-art techniques demonstrate its effectiveness and superiority. (C) 2018 Elsevier B.V. All rights reserved.

关键词： multimedia security Cyber-attack Cybersecurity Robustness Copyright protection content authentication

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Applied to Mobile multimedia Intelligent Speech System Interactive Topic Guiding Model

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IEEE access 2019年 7卷 182348-182356页

作者： Huang, Hongcheng Wang, Xupeng Hu, Min Tao, Yang Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China Chongqing Engn Res Ctr Commun Software Chongqing 400065 Peoples R China

Recently, the rapid development of mobile multimedia intelligent voice systems has affected the rapid changes in user interaction. Meanwhile, the continuous optimization of recommended algorithms makes the change of interaction mode from the original user's active search to the machine actively recommending continuously. It is an inevitable trend that multimedia intelligent voice dialogue system brings more efficient content services to users through active recommendation. However, the existing multimedia intelligent voice dialogue systems are mainly based on the user's expression content to analyze the user's subject willingness and then provide the reply contents to user. This kind of interaction only drives the topic through the user, which will cause some problems like single chat topic and missing points of interest. In order to address these problems, this paper proposes an interactive topic guiding model. This model uses the surrounding similar users and user context information to guide users' topics, and taking into account the harmony of human-computer interaction, the game method is adopted to select the optimal content response strategy by considering the user's and robot's emotional friendliness and content friendliness. The experiments show that the model proposed in this paper can effectively guide topics. Moreover, it has significant improvement in content richness compared with other models in human-machine natural interaction, which can provide some research ideas for guiding the topic in the mobile multimedia intelligent voice system.

关键词： Mobile multimedia intelligent voice system topic guide game theory

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Formulating models to survive multimedia big content from integrity violation

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Journal of Ambient Intelligence and Humanized Computing 2018年 1-10页

作者： Aljawarneh, Shadi A. Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan

With exponential growth in social media sites and high penetration of Internet access and digital computing devices, there are humongous amounts of public data being generated on a day-to-day basis. Almost real-time information regarding the emerging trends can be gained through efficient techniques/algorithms to examine this enormous amount of data, which also gives hint about early warning if there is an imminent emergency case. Also, numerous helpful indicators regarding political and socioeconomic events can be seen through by carefully mining these data, which also aid in establishing efficient public policies. However, web services and data security—protection of systems and key data to avoid unauthorised tampering—is considered to be a non-trivial real-time issue. Moreover, security issue has always been a grave issue since there is frequent conflict of service availability with the security constraints should there be a change in the service media channels. The paper’s primary focus is on new mathematical models that help in simplifying and enhancing protection for multimedia big content against unauthorised tampering by examining elements of multimedia objects. Furthermore, a multimedia big data security framework is presented in this paper. This framework, built on the basis of the proposed models, will be employed to confirm the on-fly multimedia content for a requested service/page. It is believed that such type of a framework provides a higher level of service survivability as well as user confidence. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Big data

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content-Aware Cognitive Interference Control for Urban IoT systems

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2018年第3期4卷 500-512页

作者： Baidya, Sabur Levorato, Marco Univ Calif Irvine Donald Bren Sch Informat & Comp Sci Irvine CA 92617 USA

A novel cognitive interference control framework for heterogeneous local access networks supporting computing and data processing in urban Internet of Things (IoT) systems is presented. The notion of cognitive content-aware interference control is introduced, where the transmission pattern of cognitive nodes is dynamically adapted to the state of "protected" IoT data streams. The state describes the performance degradation that interference would cause to algorithms processing the data if the cognitive nodes chose to transmit in the corresponding time period. The framework is instantiated for a case-study scenario, where device-to-device (D2D) and long-term evolution communications coexist on the same channel resource. A city-monitoring application is considered, where the state captures the type of frames within a multimedia video stream processed at the edge of the network to detect and track objects. Numerical results show that the proposed cognitive transmission strategy enables a significant throughput increase of local D2D communications for a target accuracy of the monitoring application.

关键词： Device-to-device communications LTE urban IoT video streaming spectrum sharing

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Toward Wi-Fi AP-Assisted content Prefetching for an On-Demand TV Series: A Learning-Based Approach

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IEEE TRANSACTIONS ON CIRCUITS AND systems FOR VIDEO TECHNOLOGY 2018年第7期28卷 1665-1676页

作者： Hu, Wen Jin, Yichao Wen, Yonggang Wang, Zhi Sun, Lifeng Tsinghua Univ Dept Comp Sci & Technol Tsinghua Natl Lab Informat Sci & Technol Beijing 100084 Peoples R China Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore Tsinghua Univ Grad Sch Shenzhen Shenzhen 518055 Peoples R China

The emergence of smart Wi-Fi access points (AP), which are equipped with huge storage space, opens a new research area on how to utilize these resources at the edge network to improve users' quality of experience (e.g., a short startup delay and smooth playback). One important research interest in this area is content prefetching which predicts and accurately fetches contents ahead of users' requests to shift the traffic away during peak periods. However, in practice, the different video watching patterns among users and the varying network connection status lead to the time-varying server load, which eventually makes the content prefetching problem challenging. To understand this challenge, this paper first performs a largescale measurement study on users' AP connection and TV series watching patterns using real traces. Then, based on the obtained insights, we formulate the content prefetching problem as a Markov decision process. The objective is to strike a balance between the increased prefetching and storage cost incurred by incorrect prediction and the reduced content download delay because of successful prediction. A learning-based approach is proposed to solve this problem and another three algorithms are adopted as baselines. In particular, first we investigate the performance lower bound by using a random algorithm and the upper bound by using an ideal offline approach. Then, we present a heuristic algorithm as another baseline. Finally, we design a reinforcement learning algorithm that is more practical to work in the online manner. Through extensive trace-based experiments, we demonstrate the performance gain of our design. Remarkably, our learning-based algorithm achieves a better precision and hit ratio (e.g., 80%) with about 70% (resp. 50%) cost saving compared to the random (resp. heuristic) algorithm.

关键词： Wi-Fi AP content prefetching learning-based approach

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Efficient Processing and Delivery of multimedia Data

Efficient Processing and Delivery of Multimedia Data

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作者： Tang, Linpeng Princeton University

学位级别：Ph.D.

The explosion of multimedia data on the Internet in recent years has greatly enriched people's online experience. However, it also poses a great challenge to analyze and process, and then deliver such content to the worldwide audience. This dissertation presents novel approaches to improve the overall efficiency of the stack by tailoring software design to hardware properties, as well as optimize systems by exploiting workload characteristics using learning-based approaches. First, to improve the caching performance of the flash-memory caches for content delivery network, this thesis proposes RIPQ, a framework for efficient and advanced caching with flash memory. Traditional implementations of these algorithms generate random writes that perform poorly on flash devices, decreasing the device's performance and lifespan. RIPQ overcomes this issue by aggregating small writes, colocating items with similar priorities, and perform lazy updates to achieve low over- head. By providing a priority queue interface, it allows a variety of caching algorithms to be easily implemented. Second, this thesis proposes Chess, which uses popularity prediction for higher quality video streaming. Although better encodings improve video streaming, they are also compute-intensive, and it is infeasible to encode all videos uploaded to Face- book with the highest quality codec. However, because the accesses to videos are highly skewed, we may obtain most of the benefit by only running the compute- intensive encoding on a small portion of popular videos, and the challenge lies in how to accurately and scalably run popularity prediction to detect those videos before- hand. Chess meets this demand by designing an approximate but fast base predictor with the access history information, and using an online learning method to combine multiple such predictors as well as the social signals to boost accuracy. Lastly, this thesis investigates how to accelerate deep learning models on many-core CPUs. D

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