Video Content Distribution Research Articles

Video Stream Distribution Scheme Based on Edge Computing Network and User Interest Content Model

In view of the inherent defects of traditional network protocols, unreasonable video content distribution process, content scheduling and routing mechanism need to be optimized in the current edge computing (EC) network. A centralized control EC network architecture of named data networking (NDN) is proposed in this paper. EC nodes are deployed on the edge of the network to provide caching and computing capabilities, and distribute video streams. So that transmission delay is reduced, bandwidth cost is saved, and the quality of experience (QoE) is improved. Firstly, the communication mechanism of NDN is introduced to distribute content and improve the efficiency of content distribution. In the scheme, the content distribution mechanism of video stream is improved in a distributed way. Different bit rate versions of video clips are distributed and buffered on the distributed EC nodes, so as to avoid redundant cache and save storage resources. In addition, the user interest content model is constructed. That is, video is divided into different categories according to content and user content preference model. Finally, with the limited cache and computing resources of EC nodes, the software defined network (SDN) mechanism is introduced for centralized management and control. And it provides a solution for content scheduling problem under resource constraints. In the scheme, the content distribution mode is decided by the effective arrangement of network resources, and the user's request is processed and responded cooperatively to avoid redundant cache and transmission. Based on the selection of forwarding response mode, the distributed characteristics of EC nodes and the routing and forwarding mechanism of NDN are fully utilized to provide users with response. It is shown by the experimental results that the proposed scheme optimizes the load on the network side and the experience of the client to a certain extent.

Collaboratively Replicating Encoded Content on RSUs to Enhance Video Services for Vehicles

With the development of smart cities, Internet services will be pervasively accessible for moving vehicles. It is envisioned that the video content demand of vehicles will explode in the near future. However, the strategy to efficiently distribute video content in large-scale vehicular networks is still absent due to challenges arising from the huge video population, heavy bandwidth consumption, heterogeneous user devices, and vehicles’ mobility. In this work, we propose to collaboratively replicate video content on Roadside Units (RSUs) to enhance video distribution services based on the fact that the contact period between moving vehicles and a single RSU is not long enough to complete video downloading. In our design, a video file is split into multiple chunks. Each RSU replicates a small number of original chunks and chunks encoded by network coding. Replicating encoded chunks can reduce redundancy of chunks on different RSUs so that RSUs can complement each other better, whereas original chunks can be transrated to chunks with lower bitrates flexibly to fit in users’ devices. Therefore, we replicate both original and encoded chunks on RSUs to take advantages of both sides. Stochastic models are employed to analyze chunk download processes and a convex optimization problem is formulated to determine the optimal partition of space allocated to each kind of chunks. Furthermore, we extend our strategy to support video streaming services and empirically prove that the influence caused by limitations of network coding is moderate. In the end, we conduct extensive simulations which not only validate the accuracy of our models but also demonstrate that our strategy can effectively boost video distribution services.

Prevalence of Business Models in Global OTT Video Services: A Cluster Analysis

ABSTRACTThis paper is an empirical analysis of the prevalence of business models in the Over The Top (OTT) video content distribution sector. From a review of the literature on taxonomies of OTT business models, it identifies five attributes of an OTT content distribution platform: ownership, vertical integration with content producers, platform/multiplatform compatibility, service type, and revenue model. Applying cluster analysis to SNL Kagan’s global database of 798 OTT distribution networks, the most five commonly occurring combinations of attributes are identified. The paper concludes by discussing the characteristics of these business models.

Study of Popular Music with YouTube Application: Focused on Major YouTube Music Creator in South Korea

본 논문은 온라인 중심으로 재편되고 있는 대중음악 시장의 변화와 관련하여 최근 새로운 비즈니스 모델로 부상하고 있는 유튜브를 활용한 대중음악의 홍보 방안에 관한 연구이다. 국내·외 음악 산업과 관련한 자료를 분석한 결과 대중음악 산업은 온라인 중심으로 재편되어 스트리밍 음원 서비스의 성장이 전체 음악 산업의 발전을 견인하는 역할을 하고 있으며, 특히 국내 음악 사용자는 온라인 음악 서비스 이용방법 중에서 유튜브를 가장 많이 활용하고 있음을 알 수 있었다. 또한, 국내 음악 산업은 주로 대형 기획사 중심으로 편중되어 있으나, 대형 기획사도 고비용 고수익 기반의 제작 방식에 변화가 나타나고 있으며 이러한 변화의 주요 원인은 소비자의 소비패턴 변화로부터 기인한다고 할 수 있다. 최근 주목받고 있는 다중채널네트워크 산업은 주요 플랫폼인 유튜브를 활용하여 온라인 기반 동영상 및 콘텐츠의 기획, 제작, 유통, 지원, 홍보, 수익화, 관리 등과 관련한 일련의 모든 비즈니스 분야에 이르는 사업으로 기존의 전통 적인 영상 제작 비즈니스나 광고, 방송, 홍보 등과는 다른 구조로 구성되어 있다. 이렇게 새로운 미디어를 활용하여 비교적 적은 비용과 쉬운 방법으로 음악을 홍보할 수 있는 방법들이 나타나게 되는 것은 소형 제작사나 인디 뮤지션들에게 대형 기획사 중심으로 편중된 국내 대중음악 시장에 대처할 수있는 새로운 가능성으로 대두되고 있다. 본 논문에서는 국내 음악 관련 주요 유튜브 크리에이터인 J. Fla, Jung Sung Ha, Raon Lee에 대한 개별 특징과 성공요인을 분석하였다. 이러한 분석과 연구를 바탕으로 채널 구성, 독창적인 셀프 브랜딩, 콘텐츠 품질 강화, 멀티채널의 구축, 소통 중심의 채널 운영, 세밀한 고객 분석, 정당한 수익 배분 요구등 7가지의 세부적인 방안을 도출하였다. 본 연구를 통해 새로운 미디어 환경에 대처하는 구체적인 대중음악 홍보 방안을 제시하여 자본 규모에 상관없이 국내 개인 음악 창작자와 중소형 제작 사가 능동적으로 음악을 창작하거나 제작할 수 있는 환경을 조성하고, 이를 통해 아티스트나 소형 제작사가 스스로 시장경쟁력을 갖추어 대형 기획사에 편중되어 있는 국내 대중 음악시장의 불균형을 보완할 수 있는 계기가 될 수있기를 바란다.This study aims at exploring the marketing strategies of popular music with MCN(Multi Channel Network) where YouTube, a newly emerging business model, plays a role as a main platform in consideration of the changes of popular music market being reorganized to go on online. The analysis of preceding research and data related to the music industry at home and abroad indicates that the popular music industry has been reorganized centered on online, thus the growth of music streaming service leads the development of the music industry as a whole, especially it is found that YouTube is the most preferred method of online music service among Korean music service users. Although the Korean music industry has been heavily weighted in favor of large agencies, currently they attempt to change the high-cost and high-profit production method, and the main cause of such shift comes from the changed consumption patterns among consumers. The recently growing MCN industry is a business model that uses YouTube, a major platform, to cover a series of businesses connected to the planning, production, distribution, support, marketing, making profit and management of online video clips and contents, which is structured in a different way from the traditional perspectives of video producing businesses or advertisements, broadcasting and marketing. The emergence of such an easy way to promote music by utilizing the new media at a relatively small cost suggests a new possibility to small producers or independent musicians regarding how to deal with the domestic popular music market that has been driven by large agencies. This study selected top 3 YouTube Music channels in Korea. Also it analyzed the characteristics and success factors of influential YouTube creators in the Korean music category such as J. Fla, Jung Sung Ha and Raon Lee. Also, based on such analyses and research, it drew up 7 specific strategies such as making a channel, creative self-branding, enhancing the quality of contents, establishing multi-channel, making communication-based channels, analyzing customers in detail and requesting for fair profit-sharing. By suggesting detailed marketing strategies of popular music that consider the new media environment, it wishes to create an environment where domestic individual musicians and small, medium-sized producers could actively be able to create or produce music regardless the size of capital, allowing small producers to have a chance to ease the imbalance of the Korean music industry being dominated by large agencies by equipping with market competitive power.

Efficient QoE-Aware Scheme for Video Quality Switching Operations in Dynamic Adaptive Streaming

Dynamic Adaptive Streaming over HTTP (DASH) is a popular over-the-top video content distribution technique that adapts the streaming session according to the user's network condition typically in terms of downlink bandwidth. This video quality adaptation can be achieved by scaling the frame quality, spatial resolution or frame rate. Despite the flexibility on the video quality scaling methods, each of these quality scaling dimensions has varying effects on the Quality of Experience (QoE) for end users. Furthermore, in video streaming, the changes in motion over time along with the scaling method employed have an influence on QoE, hence the need to carefully tailor scaling methods to suit streaming applications and content type. In this work, we investigate an intelligent DASH approach for the latest video coding standard H.265 and propose a heuristic QoE-aware cost-efficient adaptation scheme that does not switch unnecessarily to the highest quality level but rather stays temporarily at an intermediate quality level in certain streaming scenarios. Such an approach achieves a comparable and consistent level of quality under impaired network conditions as commonly found in Internet and mobile networks while reducing bandwidth requirements and quality switching overhead. The rationale is based on our empirical experiments, which show that an increase in bitrate does not necessarily mean noticeable improvement in QoE. Furthermore, our work demonstrates that the Signal-to-Noise Ratio (SNR) and the spatial resolution scalability types are the best fit for our proposed algorithm. Finally, we demonstrate an innovative interaction between quality scaling methods and the polarity of switching operations. The proposed QoE-aware scheme is implemented and empirical results show that it is able to reduce bandwidth requirements by up to 41% whilst achieving equivalent QoE compared with a representative DASH reference implementation.

Orchestrating Caching, Transcoding and Request Routing for Adaptive Video Streaming Over ICN

Information-centric networking (ICN) has been touted as a revolutionary solution for the future of the Internet, which will be dominated by video traffic. This work investigates the challenge of distributing video content of adaptive bitrate (ABR) over ICN. In particular, we use the in-network caching capability of ICN routers to serve users; in addition, with the help of named function, we enable ICN routers to transcode videos to lower-bitrate versions to improve the cache hit ratio. Mathematically, we formulate this design challenge into a constrained optimization problem, which aims to maximize the cache hit ratio for service providers and minimize the service delay for endusers. We design a two-step iterative algorithm to find the optimum. First, given a content management scheme, we minimize the service delay via optimally configuring the routing scheme. Second, we maximize the cache hits for a given routing policy. Finally, we rigorously prove its convergence. Through extensive simulations, we verify the convergence and the performance gains over other algorithms. We also find that more resources should be allocated to ICN routers with a heavier request rate, and the routing scheme favors the shortest path to schedule more traffic.

The History of Sharing Video Content in Poland: Analog copies of the 1980s as a Factor of Digital Peer Re-production in the 2000s

We depict practices of Polish audiences in communist Poland and the transition of these practices after the fall of communism. In Eastern Europe, digital distribution of video content has been built on semi-peripheral culture of VHS tapes copying and sharing. Although the unique Polish 20th century historical trajectory contains the experience of being excluded from Western popular culture, the first decade of 21st century brought unlimited digital access to audiovisual content. Peer re-production, a non-creative mode of participation increased. Our article provides new historical data illustrating this specificity both in terms of historical experience and globalizing technological progress.

Exploring the YouTube science communication gender gap: A sentiment analysis.

YouTube has become the second most popular web search engine (see Alexa.com ) and the primary website for individuals and organisations to freely distribute video content. Popularity statistics indicate that Science, Technology, Engineering and Mathematics-related content is of significant interest to YouTube audiences, yet analysis of the 391 most popular science, engineering and mathematics-themed channels reveals a conspicuous absence of female communicators, with the hosts of just 32 of these channels presenting as female. To help understand potential causes of this gap, analysis was conducted on popularity indicators and audience sentiments of 450 videos from 90 Science, Technology, Engineering and Mathematics-related channels. Female hosted channels were found to accumulate more comments per view, and significantly higher proportions of appearance, hostile, critical/negative and sexist/sexual commentary.

Bandwidth allocation for sharing video in a closed domestic network

To achieve an efficient and adapted network usage during video content distribution, bandwidth is classically controlled by limiting the outgoing server traffic with a token bucket policy. In this paper, we propose a predictive hull-based token bucket parameter setting in the specific context of a closed domestic network. Hull is an abstraction of the bitrate, dynamically following the bandwidth requirement evolution. The hull mechanism guarantees a maximal quality of service (QoS) for the streaming of variable bitrate encoded videos and a minimal over-reservation according to the implementation constraints. In the case of multiple simultaneous video streaming, we propose a shifting technique to reduce the impact of peaks. Postponing the streaming starting time of a video helps to decrease the maximal required bandwidth. Simulations and experiments show the effectiveness of the combined approaches to optimize online bandwidth usage, guaranteeing the best QoS (reduced delay) and quality of experience for multiple video streaming applications.

Efficient high-resolution video delivery over VANETs

The adoption of video-equipped vehicles in Vehicular ad-hoc networks (VANETs) is experiencing a rapid growth. It is also anticipated a substantial increase in the video content distribution with the arrival of self-driving cars as both passengers and vehicles will be able to produce and consume this type of media. This unveils a set of challenges, especially in VANETs where the network resources tend to be scarce and the connections suffer from time-varying error conditions. Taking everything into consideration, a Quality of Experience (QoE)-driven mechanism is desirable to enhance the video delivery over error-prone networks. To this end, the combined use of forward error correction and unequal error protection has proven its efficiency in delivering high-quality videos with low network overhead. The proposed intelligent quality-driven and network-aware mechanism (AntArmour) uses an ant colony optimization scheme to dynamically allocate a precise amount of redundancy. This allows AntArmour to safeguard, in real-time, the live transmission of high-resolution video streams. This operation is performed according to specific high efficiency video coding details and the actual network conditions such as the signal-to-noise ratio, the network density, the vehicle’s position, and the current packet loss rate (PLR) as well as the prediction of future PLR. The experiments were performed using real map’s clippings and actual video footage. The assessment was performed with the aid of two well-known objective QoE metrics, as well as the measure of the network overhead. The results showed that the proposed mechanism outperformed all its competitors in both video quality improvement and network overhead decrement.

An SDN-Based Video Multicast Orchestration Scheme for 5G Ultra-Dense Networks

Predictions indicate that mobile video streams will dominate mobile traffic in the near future. Heterogeneous UDN is a promising technique for 5G mobile networks. This technique offers high access speeds for video watchers through the dense deployment of small cells. However, the backhaul of a UDN will become a bottleneck with the rapid increase in mobile video traffic. Additionally, the considerable video traffic generated from and to UDNs induces significant stress on the core network of a 5G network. Therefore, it is necessary to optimize the distribution of video content for 5G UDNs. Multicasting is considered to be a good communication method for distributing video content. This article examines a video multicast orchestration scheme based on SDN, named HCM, for 5G UDNs. To adapt well to a heterogeneous UDN environment in 5G networks and effectively save spectrum resources, HCM implements wireless multicast functionality based on a common unicast channel. HCM performs concurrent multicasting to clearly reduce the bandwidth consumption of a 5G network, particularly the backhaul, and further controls video traffic passed through the backhaul using a traffic-aware method. During the packet loss recovery of HCM, SDN-based elastic area multicast is used to more effectively retransmit recovered packets. The experimental results indicate that our proposed scheme can distribute video content with high effectiveness.

How Big Are Your Dreams? Gauging the Size of Future Content [The Art of Storage

Many trends are driving the development of modern rich media. The digital storage and bandwidth requirements to capture, process, distribute, and archive digital video content are growing. One of the biggest trends is a move to higher resolution content, with 4K content becoming common in consumer displays and lots of early work taking place on 8K content. Another is higher frame rates, with camera capture rates at 48 or 60 frames per second (fps) and 4K acquisition up to 120 fps; higher frame rates are used in specialty cameras designed for slow-motion capture. In addition, cameras and display devices are sporting higher dynamic range and an expanded color space, providing content that looks even more like real life.

Flexible Caching Algorithms for Video Content Distribution Networks

Global video content distribution networks (CDNs) serve a significant fraction of the entire Internet traffic. Effective caching at the edge is vital for the feasibility of these CDNs, which can otherwise incur substantial costs and overloads in the Internet. We analyze the challenges and requirements for content caching on the servers of these CDNs which cannot be addressed by standard solutions. We design multiple algorithms for this problem: a LRU-based baseline to address the requirements; a flexible ingress-efficient algorithm; an offline cache aware of future requests (greedy) to estimate the maximum efficiency we can expect from any online algorithm; an optimal offline cache (for limited scales); and an adaptive ingress control algorithm for reducing the server's peak upstream traffic. We use anonymized actual data from a global video CDN to evaluate the algorithms and draw conclusions on their suitability for different settings.

FD device‐to‐device communication for wireless video distribution

Spectrum scarcity and dramatically increasing demand for high data rate and high-quality video live streaming are of future cellular network design challenges. As a solution to this problem, cache-enabled cellular network architecture has been recently proposed. Device-to-device (D2D) communications can be exploited for distributed video content delivery, and devices can be used for caching of the video files. This can increase the capacity and reduce the end-to-end delay in cellular networks. In this study, the authors propose a new scheme for video distribution over cellular networks by exploiting full-duplex (FD) radios for D2D devices in two scenarios: (i) two nodes exchange their desired video files simultaneously and (ii) each node can concurrently transmit to and receive from two different nodes. In the latter case, an intermediate transceiver can serve one or multiple users’ file request(s) whilst capturing its desired file from another device in the vicinity. Mathematical expressions along with extensive simulations are used to compare their proposed scheme with a half-duplex scheme to show the achievable gains in terms of sum throughput, active links, and delay. They will also look into the energy cost for achieving the improvements provided by operation in FD mode.

Integrated home video content procurement and distribution planning under uncertainty

In home video supply chains, movie makers as content producers create the most value in the movie industry but they are unable to publish and distribute their videos in an efficient while effective manner. Therefore, they usually sell their videos’ licenses to home video enterprises (HVEs) that have expertise in advertising and distributing the procured videos to the market. This paper proposes a novel integrated model for content procurement and distribution planning within the context of home video supply chains. The developed model helps HVEs to determine buying which available videos are more profitable and how to distribute them in terms of distribution timing and quantities. It also determines videos’ license prices, which is certainly one of the most important concerns of HVEs in this context. According to deep uncertainty in videos’ demands, fluctuation in audiences’ expectations and diverse qualities in the content of procured videos, the critical parameters are tainted with epistemic uncertainty and therefore are formulated though possibility distributions in the form of triangular fuzzy numbers. An efficient possibilistic programming approach is tailored to convert the original possibilistic model into the crisp counterpart. Applicability of the proposed model is validated through an illustrative example from which useful managerial insights are also provided.

A Window-Based, Server-Assisted P2P Network for VoD Services with QoE Guarantees

We describe a Peer-to-Peer (P2P) network that is designed to support Video on Demand (VoD) services. This network is based on a video-file sharing mechanism that classifies peers according to the window (segment of the file) that they are downloading. This classification easily allows identifying peers that are able to share windows among them, so one of our major contributions is the definition of a mechanism that could be implemented to efficiently distribute video content in future 5G networks. Considering that cooperation among peers can be insufficient to guarantee an appropriate system performance, we also propose that this network must be assisted by upload bandwidth from servers; since these resources represent an extra cost to the service provider, especially in mobile networks, we complement our work by defining a scheme that efficiently allocates them only to those peers that are in windows with resources scarcity (we called it prioritized windows distribution scheme). On the basis of a fluid model and a Markov chain, we also developed a methodology that allows us to select the system parameters values (e.g., windows sizes or minimum servers upload bandwidth) that satisfy a set of Quality of Experience (QoE) parameters.

Design, Implementation, and Measurement of a Crowdsourcing-Based Content Distribution Platform

Content distribution, especially the distribution of video content, unavoidably consumes bandwidth resources heavily. Internet content providers invest heavily in purchasing content distribution network (CDN) services. By deploying tens of thousands of edge servers close to end users, CDN companies are able to distribute content efficiently and effectively, but at considerable cost. Thus, it is of great importance to develop a new system that distributes content at a lower cost but comparable service quality. In lieu of expensive CDN systems, we implement a crowdsourcing-based content distribution system, Thunder Crystal, by renting bandwidth for content upload/download and storage for content cache from agents. This is a large-scale system with tens of thousands of agents, whose resources significantly amplify Thunder Crystal’s content distribution capacity. The involved agents are either from ordinary Internet users or enterprises. Monetary rewards are paid to agents based on their upload traffic so as to motivate them to keep contributing resources. As far as we know, this is a novel system that has not been studied or implemented before. This article introduces the design principles and implementation details before presenting the measurement study. In summary, with the help of agent devices, Thunder Crystal is able to reduce the content distribution cost by one half and amplify the content distribution capacity by 11 to 15 times.

Multivariate Data Fusion-Based Learning of Video Content and Service Distribution for Cyber Physical Social Systems

Integration of physical processes with the computing world is driving newer challenges for networking frameworks. Cyber physical social systems (CPSSs) are another upcoming paradigm that encompasses the ever-growing interaction between the physical, social, and cyber worlds. As communication networks form the basis of these interactions, a cognitive evaluation of networks is called for. This CPSS driven network evolution was a direction motivating this paper. With the implementation of the next generation networks, traffic from real-time interactive services, such as video conferencing, is surpassing those of conventional transactional services. As such multimedia data transportation over IP networks has stringent quality constraints in terms of required bandwidth, latency, and jitter, legacy networks with no quality of service face challenges in terms of performance. We attempt to perform a multivariate analysis of video call record data collected from a wide area organizational network over a period of time. Learning-based prediction is attempted by training four classifiers: naive Bayes, $k$ -nearest neighbor, decision tree, and support vector machine. Two independent set of experiments were conducted with oversights of bandwidth and destination prediction. Both the discrete and continuous valued predictors were involved in the training. Performance evaluation of the generated hypothesis in both the cases was conducted using tenfold cross validation. Combined analysis using the assorted combinations of attributes was conducted, and thereafter, the effect of each feature was evaluated through singular attribute portioning. This paper presents observations, which exhibit deviations from the conventional machine learning paradigms. An attempt to increase the prediction accuracy of the classifiers was made through the boosting ensemble methodology. However, miniscule addition in performance was achieved. A maximum prediction accuracy of 81% for bandwidth and 60% for destination was obtained. Reasons of low accuracy of conventionally better performing algorithm were reasoned with a mathematical comprehension. Divergence of the obtained results from the accepted patterns poses an open research problem, particularly with respect to the nature and peculiarities of the data set. The proposed learning technique can have potential applications in social, tactical, and strategic spheres.

Parallel video transcoding using Hadoop MapReduce

Video transcoding has become a key technology for video content distribution network service. In this paper, we propose a novel MapReduce-based parallel video transcoding method. In our method, video files are stored on a shared file system to reduce the overhead of disks I/O and networks in the Hadoop MapReduce. FFmpeg is used to compute the splitting point of the video and the actual video transcoding. Experimental results show that our method can significantly reduce the time of transcoding.

Dynamic Strategy Selection based on Evidence Theory. Application on a P2P-VoD system

Peer-to-Peer Video-on-Demand (VoD) systems are rising as a new dominant way to distribute video content over IP networks. While many works addressed the broad area of P2P communications, few work has been focused onto the resource allocation in P2P streaming systems, where the real-time constraint adds another dimension to the problem. Most work on P2P resource allocation solve the problem with static rules strategies are not robust when they are facing to the content demand (popularity) trends. In this paper, we use a dynamic approach, which automatically selects the best strategy on a given time period. With the proposed P2P resource allocation method, a VoD service provider can combine any number of resource allocation strategies and formulate different performance objectives that meet its requirements. As part of our effort to develop algorithms that can be effectively deployed by broadband operators, we developed a full-scale emulator for P2P streaming that can support up to 10,000 peers. The P2P streaming emulator includes a VoD request generators, along with a SuperNode (with backend DataBase) that tracks and allocates network resources. In this paper the goal is to use a low complexity learning approach to enhance the performance of a P2P-VoD system based on evidence theory. The proposed approach is compared to both static and dynamic solutions. The obtained results show that the proposed approach allows to reduce the complexity of selecting resource allocation strategies, while maintaining a low rejection rate.