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Research on Digital Concert Performance System based on Computer Virtual Reality Technology

Research on Digital Concert Performance System based on Comp...

引用

Networking, Communications and Information Technology (NetCIT), International Conference on

作者： Wen Lei Leshan Normal University Leshan China

This paper will analyze the development status of intelligent orchestration according to the actual working situation and existing problems of the digital concert performance system, and give solutions according to the problems. In this paper, a music management platform is developed in a targeted manner, so as to realize the processing of music works, which fully involves cataloging management, transcoding services and other aspects, and further forms the association table of music works. This paper discusses the similarity between the two groups of target users and ordinary users. Under the action of this paper, the user's interest in the works can be known, and the recommended T list is finally obtained, thus a complete digital concert performance system is obtained.

关键词： Computer simulation Virtual reality transcoding Manuals Information and communication technology Motion control Creativity

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VOD2Live: Exploring the technical challenges in the adaptive generation of linear FAST channels from existing on-demand and live content and personalised ad selection

VOD2Live: Exploring the technical challenges in the adaptive...

引用

IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Eduardo Saiz Karen Troiano Torbjö rn Einarsson Pablo Curiel Agile Content S.A. Bilbao Spain Edgeware - Agile Content S.A. Stockholm Sweden

With the streaming services consumption on the rise, Free Ad-Supported TV (FAST) channels are becoming one of the most demanded services with benefits for all stakeholders: Content providers and advertisers have in FAST channels the opportunity to develop new business models while viewers benefit from the access to high-quality contents for free. Despite this not being a new concept, as ad-supported streaming technology has been in the market for a while now, there are still some issues that must be tackled when trying to evolve this market to the next level. VOD2Live, the novel service platform by AgileContent, is aimed at content providers for the quick and cost-effective generation of linear FAST channels from both live streams and on-demand assets. The solution is cost-effective by creating dynamic linear channels without the need for live transcoding, and instead stitching segments by combining video GoPs and corresponding audio frame intervals. VOD2Live offers features such as adaptive selection of geographically disperse assets in the cloud, the smart addition, repetition and/or substitution of contents on the fly on FAST channels already deployed, or even the dynamic relocation of ad-slots in the planned rundown. This paper aims to present and further explore the overall technical challenges of the platform.

关键词： Video coding TV Costs transcoding Streaming media Media Complexity theory

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A Knowledge Graph-based Cooperative Caching Scheme in MEC-enabled Heterogeneous Networks

A Knowledge Graph-based Cooperative Caching Scheme in MEC-en...

引用

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Yalu Bai Dan Wang Bin Song State Key Laboratory of Integrated Services Networks Xidian University Xi'an Shaanxi China

ISBN: (纸本)9781665435413

To meet the user demand for high-speed and low-latency video services, this paper envisions a cooperative caching and video transcoding architecture in the multi-access edge computing (MEC)-enabled heterogeneous network. Under this architecture, we propose a novel knowledge graph (KG)-based video caching scheme. Specifically, KG reveals the relation between videos, which acts as an external knowledge to reflect user preferences thus guiding caching decisions. The goal of this paper is to minimize the service delay within the caching and computing resource constraints. To this end, we combine KG with the deep reinforcement learning (DRL) method and design a KG-deep Q network (DQN) based caching algorithm. KG is designed to select the related videos as candidate actions for DQN's caching decision. This way improves the convergence performance of DRL while providing rich external references for caching decisions. Numerous simulation results demonstrate that the proposed algorithm outperforms the traditional baselines on cache hit rate and delay performance.

关键词： Knowledge engineering Multi-access edge computing Cooperative caching Simulation Reinforcement learning Computer architecture transcoding

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Hybrid P2P-CDN Architecture for Live Video Streaming: An Online Learning Approach

Hybrid P2P-CDN Architecture for Live Video Streaming: An Onl...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Reza Farahani Abdelhak Bentaleb Ekrem Çetinkaya Christian Timmerer Roger Zimmermann Hermann Hellwagner Christian Doppler Laboratory ATHENA Institute of Information Technology Alpen-Adria-Universitat Klagenfurt Austria School of Computing National University of Singapore Singapore

ISBN: (纸本)9781665435413

Designing a cost-effective, scalable, and flexible architecture that supports low latency and high quality live video streaming is still a challenge for Over-The-Top (OTT) service providers. To cope with this issue, this paper leverages Peer-to-Peer (P2P), Content Delivery Network (CDN), edge computing, Network Function Virtualization (NFV), and distributed video transcoding paradigms to introduce a hybRId P2P-DN arcHfiTecture for livE video stReaming (RICHTER). We first introduce RICHTER's multi-layer architecture and design an action tree that considers all feasible resources provided by peers, edge, and CDN servers for serving peer requests with minimum latency and maximum quality. We then formulate the problem as an optimization model executed at the edge of the network. We present an Online Learning (OL) approach that leverages an unsupervised Self Organizing Map (SOM) to (i) alleviate the time complexity issue of the optimization model and (ii) make it a suitable solution for large-scale scenarios, by enabling decisions for groups of requests instead of for single requests. Finally, we implement the RICHTER framework, conduct our experiments on a large-scale cloud-based testbed including 350 HAS players, and compare its effectiveness with baseline systems. The experimental results illustrate that RICHTER outperforms baseline schemes in terms of users' Quality of Experience (QoE), latency, and network utilization, by at least 59%, 39%, and 70% respectively.

关键词： Self-organizing feature maps Computer architecture transcoding Peer-to-peer computing Network function virtualization Service-oriented architecture Quality of experience

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Proactive Edge Computing for Video Streaming: A Mutual Conversion Model for Varying Requirements on Representations

Proactive Edge Computing for Video Streaming: A Mutual Conve...

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International Conference on Computer and Communications (ICCC)

作者： Guangzheng Xiong Zhitao Dai School of Computer Science Beijing University of Posts and Telecommunications Beijing China

The vast proliferation of video streaming traffic imposed unprecedented challenges to origin servers in the Core Network (CN), further exacerbated by the diversity of requirements on video encoding format, resolution, and frame rate. Multi-access Edge Computing (MEC) has been introduced to mitigate the problem by storing and transcoding videos at the network edges to reduce traffic to CN. Traditional approaches hardly convert videos mutually or successively, limiting the overall performance of edge servers. By applying the space-time video super-resolution (STVSR) algorithm and transcoding on edge servers, this work proposes a novel mutual conversion model with a conversion graph among representations. The model measures and estimates the qualities of conversion outputs to maintain high Quality of Experience (QoE). Moreover, an off-peak proactive cache replacement algorithm is introduced to utilize remaining resources by prefetching and pre-converting popular representations, catering to the changing interests of clients. Transmission history is also collected to predict band-width and playback buffer length. The experimental results demonstrate that the proposed approach significantly reduces traffic to the origin server while achieving high QoE.

关键词： Multi-access edge computing Limiting Prefetching Superresolution transcoding Streaming media Prediction algorithms

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Position Paper: Renovating Edge Servers with ARM SoCs

Position Paper: Renovating Edge Servers with ARM SoCs

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IEEE/ACM Symposium on Edge Computing (SEC)

作者： Mengwei Xu Li Zhang Shangguang Wang Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781665486118

ISBN: (纸本)9781665486125

Edge servers are key to the success of edge computing. Compared to cloud servers, edge servers suffer from a more constrained and costly electricity supply due to their dense, near-population deployment. Towards higher energy efficiency, we propose an extreme design of edge servers - SoC-Cluster that consists of massive, inter-connected ARM SoCs. Indeed, such SoC-Clusters have already been adopted to serve the cloud gaming application in the wild. In this paper, we present a concrete implementation of a COTS SoC-Cluster and its hardware specifications. We then discuss the potential killer applications that such SoC-Cluster can well serve and the major challenges to be solved. We also dive deep into two of such applications (live video transcoding and deep learning serving) and carry out a measurement study to demystify the application performance of SoC-Cluster. The results reveal that, compared to traditional servers, SoC-Cluster not only can reduce energy consumption but even deliver higher workload throughput in certain scenarios. Finally, we conclude the paper and discuss the primary research directions that can be explored by our community from applications, software, and hardware aspects.

关键词： Deep learning Energy consumption Program processors transcoding Cloud gaming Throughput Hardware

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EFFICIENT HEVC-TO-VVC TRANSCODER BASED ON A BAYESIAN CLASSIFIER FOR THE FIRST QUADTREE DEPTH LEVEL

EFFICIENT HEVC-TO-VVC TRANSCODER BASED ON A BAYESIAN CLASSIF...

引用

IEEE International Conference on Image Processing (ICIP)

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 Oviedo Dept Comp Sci Oviedo Spain Univ Politecn Madrid ETS Ingn Informat Madrid Spain Cardiff Metropolitan Univ Sch Technol Cardiff Wales

ISBN: (纸本)9781728163956

In the coming years, the Versatile Video Coding (VVC) standard will be launched to replace the current High Efficiency Video Coding (HEVC) standard, making it necessary to find efficient methods to convert existing multimedia content to the new format. However, transcoding is a complex pipeline composed of a decoding and an encoding process that involves long processing times. On the basis of the existing correlation between the block partitioning structures of both standards, this paper presents an HEVC-to-VVC transcoding scheme. The proposed method consists of a Naive-Bayes classifier that assists the partitioning decision at the first level of quadtree by using features extracted from the 128x128 pixel blocks of the residual and reconstructed frames in HEVC. The experimental results using random access configuration show an average transcoding time reduction of 13.38% at the cost of a compression efficiency loss of 0.32% in terms of BD-rate.

关键词： HEVC H.265 VVC transcoding MTT

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Deconstructing Google's Web Light Service 20

Deconstructing Google's Web Light Service

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29th Web Conference (WWW)

作者： Tahir, Ammar Munir, Muhammad Tahir Malik, Shaiq Munir Qazi, Zafar Ayyub Qazi, Ihsan Ayyub LUMS Dept Comp Sci SBASSE Lahore Pakistan

ISBN: (纸本)9781450370233

Web Light is a transcoding service introduced by Google to show lighter and faster webpages to users searching on slow mobile clients. The service detects slow clients (e.g., users on 2G) and tries to convert webpages on the fly into a version optimized for these clients. Web Light claims to significantly reduce page load times, save user data, and substantially increase traffic to such webpages. However, there are several concerns around this service, including, its effectiveness in, preserving relevant content on a page, showing third-party advertisements, improving user performance as well as privacy concerns for users and publishers. In this paper, we perform the first independent, empirical analysis of Google's Web Light service to shed light on these concerns. Through a combination of experiments with thousands of real Web Light pages as well as controlled experiments with synthetic Web Light pages, we (i) deconstruct how Web Light modifies webpages, (ii) investigate how ads are shown on Web Light and which ad networks are supported, (iii) measure and compare Web Light's page load performance, (iv) discuss privacy concerns for users and publishers and (v) investigate the potential use of Web Light as a censorship circumvention tool.

关键词： Web Light transcoding page load privacy censorship circumvention

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Referenceless Rate-Distortion Modeling with Learning from Bitstream and Pixel Features 20

Referenceless Rate-Distortion Modeling with Learning from Bi...

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28th ACM International Conference on Multimedia (MM)

作者： Sun, Yangfan Li, Li Li, Zhu Liu, Shan Univ Missouri Kansas City MO 64110 USA Tencent Amer Palo Alto CA USA

ISBN: (纸本)9781450379885

Generally, adaptive bitrates for variable Internet bandwidths can be obtained through multi-pass coding. Referenceless predictionbased methods show practical benefits compared with multi-pass coding to avoid excessive computational resource consumption, especially in low-latency circumstances. However, most of them fail to predict precisely due to the complex inner structure of modern codecs. Therefore, to improve the fidelity of prediction, we propose a referenceless prediction-based R-QP modeling (PmR-QP) method to estimate bitrate by leveraging a deep learning algorithm with only one-pass coding. It refines the global rate-control paradigm in modern codecs on flexibility and applicability with few adjustments as possible. By exploring the potentials of bitstream and pixel features from the prerequisite of one-pass coding, it can reach the expectation of bitrate estimation in terms of precision. To be more specific, we first describe the R-QP relationship curve as a robust quadratic R-QP modeling function derived from the Cauchybased distribution. Second, we simplify the modeling function by fastening one operational point of the relationship curve received from the coding process. Third, we learn the model parameters from bitstream and pixel features, named them hybrid referenceless features, comprising texture information, hierarchical coding structure, and selected modes in intra-prediction. Extensive experiments demonstrate the proposed method significantly decreases the proportion of samples' bitrate estimation error within 10% by 24.60% on average over the state-of-the-art.

关键词： Rate-distortion modeling referenceless rate-distortion model machine learning transcoding video processing

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Cost-Efficient Storage for On-Demand Video Streaming on Cloud 6

Cost-Efficient Storage for On-Demand Video Streaming on Clou...

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6th IEEE Virtual World Forum on Internet of Things (IEEE WF-IoT)

作者： Darwich, Mahmoud Ismail, Yasser Darwich, Talal Bayoumi, Magdy Bloomsburg Univ Penn Dept Math & Digital Sci Bloomsburg PA 17815 USA Southern Univ & A&M Coll Elect Engn Dept Baton Rouge LA 70807 USA Microchip Technol Inc San Jose CA 95134 USA Univ Louisiana Lafayette Dept Elect & Comp Engn Lafayette LA 70504 USA

ISBN: (纸本)9781728155036

Video stream is converted to several formats to support the user's device, this conversion process is called video transcoding, which imposes high storage and powerful resources. With emerging of cloud technology, video stream companies adopted to process video on the cloud. Generally, many formats of the same video are made (pre-transcoded) and streamed to the adequate user's device. However, pre-transcoding demands huge storage space and incurs a high-cost to the video stream companies. More importantly, the pre-transcoding of video streams could be hierarchy carried out through different storage types in the cloud. To minimize the storage cost, in this paper, we propose a method to store video streams in the hierarchical storage of the cloud. Particularly, we develop a method to decide which video stream should be pre-transcoded in its suitable cloud storage to minimize the overall cost. Experimental simulation and results show the effectiveness of our approach, specifically, when the percentage of frequently accessed videos is high in repositories, the proposed approach minimizes the overall cost by up to 40%.

关键词： cloud storage video stream pre-transcoding clustering transcoding

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