Multimedia Content Distribution Research Articles

Broadcast/multicast delivery integration in B5G/6G environments

This paper describes the design of a Broadcast Core Network (BCN). The BCN is intended to enable the integration of existing terrestrial broadcast systems into a 5G/6G ecosystem. The existing multimedia content distribution architecture in current broadcast networks and the capabilities of 5G access and core networks (5GC) are analyzed to dissect their limitations. We show, inter alia, how the lack of an equivalent data plane makes it impossible to import a 5GC as a solution. We propose a dynamic model that addresses the necessary structure for integrating a BCN in today’s broadcast networks. The paper then considers the evolution of the BCN up to its integration into a 5G/6G cellular network ecosystem. The required architectural components are described. Detailed definitions of the microservices are included, along with the relationships and message calls to enable relevant use cases. Finally, a simulation and lab prototype were developed to validate the proposal. The lab platform is based on multimedia content broadcasting nodes using ATSC 3.0 and a BCN prototype for its control. The results demonstrate the ability of the proposed solution to maximize spectrum utilization and improve the efficiency of current systems.

Methodological Approach for Identifying Websites with Infringing Content via Text Transformers and Dense Neural Networks

The rapid evolution of the Internet of Everything (IoE) has significantly enhanced global connectivity and multimedia content sharing, simultaneously escalating the unauthorized distribution of multimedia content, posing risks to intellectual property rights. In 2022 alone, about 130 billion accesses to potentially non-compliant websites were recorded, underscoring the challenges for industries reliant on copyright-protected assets. Amidst prevailing uncertainties and the need for technical and AI-integrated solutions, this study introduces two pivotal contributions. First, it establishes a novel taxonomy aimed at safeguarding and identifying IoE-based content infringements. Second, it proposes an innovative architecture combining IoE components with automated sensors to compile a dataset reflective of potential copyright breaches. This dataset is analyzed using a Bidirectional Encoder Representations from Transformers-based advanced Natural Language Processing (NLP) algorithm, further fine-tuned by a dense neural network (DNN), achieving a remarkable 98.71% accuracy in pinpointing websites that violate copyright.

Intelligent multimedia content delivery in 5G/6G networks: A reinforcement learning approach

AbstractMultimedia content in 5G/6G networks makes safe, confidential, and efficient content delivery difficult. Intelligent systems that adapt to the ever‐changing network environment are needed to distribute multimedia content in these networks. Reinforcement learning (RL) can optimize multimedia content distribution based on network congestion, capacity, and user preferences. This study proposes RL‐based intelligent multimedia content distribution. RL algorithms learn from the network environment and generate optimum judgments incorporating several aspects of the suggested framework. The framework delivers multimedia material securely and privately with great quality. This study provides an intelligent multimedia content delivery architecture that uses RL approaches to solve 5G/6G content delivery problems. This research presents an RL system optimized with the double DQN algorithm having a reward of 51604.93 in 7000 episodes for efficient video file sharing on intracity buses. The RL agent balances network congestion and bandwidth by leveraging multiple sources such as bus and intersection caches and base stations, improving secure multimedia content delivery in 5G/6G networks and enhancing the passenger experience. The study confirms the system's effectiveness using reward and loss metrics and identifies potential future research directions. Future work could explore additional RL algorithms, scalability for larger networks, complex delivery scenarios, and integration with blockchain and edge computing for improved security and efficiency in multimedia content delivery.

Smart optimization in 802.11p media access control protocol for vehicular ad hoc network

<span lang="EN-US">The innovative idea presented in this research is that advancements in automotive networks and embedded devices can be used to assess the impact of congestion control on throughput and packet delivery ratio (PDR), or so-called multimedia content delivery. Vehicle networking and the distribution of multimedia content have become essential factors in getting packets to their intended recipients due to the availability of bandwidth. Vehicle-to-infrastructure (V2I) communication systems are crucial in vehicular ad hoc networks (VANETs), which permit vehicles to connect by distributing and delivering traffic data and transmission packet schemes. High levels of mobility and changing network topology necessitate dispersed monitoring and execution for congestion control. The amount of traffic congestion for packet transfers could be reduced by enhancing congestion management in terms of throughput and PDR percentages. In a highway setting, the Taguchi approach has been used to optimize the parameters for congestion control. Based on throughput and PDR performance measures, this technique minimizes superfluous traffic information and lowers the likelihood of network congestion. The simulation results have shown that the proposed approach performs better since it increases network performance while effectively utilizing bandwidth. The effectiveness of the suggested technique is evaluated using a typical VANETs scenario for V2I communication while driving on a highway.</span>

An Update From the 5G Media Action Group (5G-MAG)

Connectivity is becoming a key element in the production and distribution of multimedia content to address audiences worldwide. The development and use of technologies traditionally optimized for specific media applications are steadily being replaced by technologies playing in the global consumer market domain and able to rapidly evolve. The plethora of devices now able to consume media content, and the freedom given to users in doing so is massive. Connected devices now include smart TVs, PCs, smartphones, tablets, car infotainment systems, smart speakers, as well as smart glasses, or head-mounted devices. The focus on media content is shifting from just linear to more interactive, social, even gaming-like, location-dependent, targeted, personalized and immersive.

Advanced NOMA-Based RRM Schemes for Broadcasting in 5G mmWave Frequency Bands

A relevant solution for the high demand for new multimedia applications and services is millimeter wave (mmWave) frequency band in 5G. However, in order to face the technological challenges of the present and those that will appear in the short-term future, it is necessary to improve the spectral efficiency of 5G systems. In particular, the Radio Resource Management (RRM) module is considered an essential component. Nevertheless, resource allocation techniques that combine orthogonal multiplexing (OMA) schemes, such as Time Division Multiple Access (TDMA), with Non-Orthogonal Multiple Access (NOMA) techniques have not been studied in-depth. Therefore, this article designs and evaluates different RRM models that combine TDMA with NOMA for innovative applications in the 5G mmWave frequency bands. To this end, the advantages and challenges associated with mmWave frequency bands and potential future applications are introduced. An innovative use case has been designed to test the RRM models. The use case is based on the on-demand distribution of multimedia content in high-density environments, such as museum halls. The on-demand content aims at offering Augmented Reality (AR) services to unicast users in order to provide personalized services. The models have been evaluated in terms of throughput, capacity, and availability. According to the results, the combined RRM techniques offer up to 50% more capacity than the single multiplexing technology models and can provide video-on-demand service to practically the entire cell.

Time-Critical Data Dissemination Under Flash Crowd Traffic

How to rapidly disseminate a large-sized file to many recipients in flash crowd arrival patterns is a fundamental challenge in many applications, such as distributing multimedia content. To tackle this challenge, we present the Bee, which is a time-critical peer-to-peer data dissemination system aiming at minimizing the maximum dissemination time for all peers to obtain the complete file in flash crowd arrival patterns. Bee is a decentralized system that organizes peers into a randomized mesh-based overlay, and each peer only works with local knowledge. We introduce the slowest peer first strategy to boost the speed of dissemination and present a topology adaptation algorithm that adapts the number of connections based on upload bandwidth capacity of a peer. Bee is designed to support network heterogeneity and deals with the flash crowd arrival pattern without sacrificing the dissemination speed. We also show the lower bound analysis of the data dissemination problem, and present the experimental results to demonstrate that the performance of Bee can roughly approximate the lower bound of the data dissemination problem under flash crowd traffic.

Development of Popular Science Journalism in the Cross-Media Context

The development of information and communication technologies and the digitalization of the media space create new opportunities for the popularization of scientific knowledge. There is an objective need to study popular science journalism as an activity that develops a multimedia network format and various media content distribution channels. The work is aimed at analyzing the features of the development of popular science journalism on the Internet in the context of cross-media. The study employed general scientific methods (analysis and synthesis), a method of studying documents with elements of content analysis, a comparative method that includes a comparison of different types of content, as well as data from social network and messenger analytics services. The authors studied media channels and ways to promote media content, its ideological, thematic, and structural characteristics, the features of its transformation on high citation index ratings scientific-oriented platforms Nplus1.ru and Naked-science.ru. As a result of the conducted research, it was discovered that the audience involved in the studied online media is carried out through a differentiated cross-media presence, the use of rewriting, and reposting processes. Particular attention was focused on the comparative analysis of the media content of official websites, accounts in the social network VKontakte, and Telegram messenger.

FASE: Fine-Grained Accountable and Space-Efficient Access Control for Multimedia Content With In-Network Caching

To reduce the duplicated traffic and improve the performance of distributing massive volumes of multimedia contents, in-network caching has been proposed recently. However, as in-network content caching can be directly utilized to respond users’ requests, multimedia content retrieval is beyond content providers’ control and makes it hard for them to implement access control and service accounting. In this paper, we propose a Fine-grained Accountable and Space-Efficient access control scheme, called FASE, for multimedia content distribution. FASE allows content providers to be fully offline while making the best of in-network caching. In FASE, the attribute-based encryption at multimedia content provider side and access policy based authentication at the edge router side jointly ensure secure fine-grained access control. Our scheme is efficient in both space and time. By designing one time chameleon signature (OTCS), users can keep anonymous during the authentication, and their privileges can be conveniently revoked when needed. Besides, secure service accounting is implemented by letting edge routers collect service credentials generated during users’ request process. Through formal security analysis, we prove the security of our scheme. Simulation results demonstrate that our scheme is efficient with acceptable overhead.

Blockchain-based multimedia content distribution with the assured system update mechanism

Blockchain-based multimedia content distribution with the assured system update mechanism

Data-driven multimedia edge network and content delivery

Recently, network multimedia has undergone rapid developments. Compared with traditional end-to-end multimedia content services, user behaviors in emerging mobile multimedia services have experienced fundamental changes. For efficient mobile multimedia content services, the conventional content distribution architecture relies on a centralized infrastructure, in which global popularity trends cannot be effectively obtained. The use of edge network architecture combined with data-driven strategies to aid the dynamic mobile multimedia content has become a new choice for improved service quality. Using data-driven and cross-domain collaboration methods, this paper reveals a set of principles of mobile multimedia content service and system architecture design. This paper first discusses data-driven behavior patterns and network performance analysis. It shows the essential difference between edge multimedia content distribution and traditional content distribution. Further, this paper presents general edge multimedia research methodologies, including a framework of multimedia edge network content distribution using data-driven and cross-domain collaboration methods. Finally, this paper provides several representative frameworks, including SocialCDN, which uses social information for content distribution, CPCDN, in which a content-provider-based content delivery framework is proposed, EdgeCDN, which is an edge content delivery framework, and finally CrowdCDN, which uses user-based crowdfunding resources for efficient content distribution.

Additive deep feature optimization for semantic image retrieval

Rapid increase in the distribution of multimedia content in recent times presents a challenging problem for content-based image retrieval systems. Image contents, such as position and shape of objects alongside contextual features such as background can be used to retrieve visually similar images. Variations in contrast, color, intensity and texture of contextually similar images make it an interesting research problem. A deep convolutional neural network-based model called MaxNet for content-based image retrieval is presented in this paper. The proposed system bypasses the reliance on handcrafted features and extracts deep features directly from the images, which are then used to retrieve contextually similar images from the database. The proposed MaxNet model is built by stacking the updated inception module in a hierarchical fashion. Features extracted from various pipelines in the inception module are aggregated after each inception maximizing the feature values. This novel aggregation step generates a model that is able to adapt to variety of datasets. Various types of aggregations are discussed in this study. Model overcomes the over-fitting problem by using a dropout layer after each inception block and just before the output layer. The system outputs softmax probabilities, which are stored in the feature database and are used to compute the similarity index to retrieve images similar to the query image. The MaxNet model is evaluated using four popular image retrieval datasets namely, Corel-1k, Corel-5k, Corel-10k and Caltech-101, where it outperforms state-of-the-art methods in key performance indicators.

Crossover-Based Improved Sine Cosine Algorithm for Multimedia Content Distribution in Cloud Environment

Widespread growth of multimedia content distribution can increase the cost and time of the content distribution. The multimedia services are stored by the service provider and the user is provided based on their demand. Basically, the network user number increases quickly, and the response time for huge numbers of users also increases rapidly. Therefore, the need of service cannot be reached. Initially, we solicited notable extraction technique to collect the interest features of user. The adjacent region and similar service interests of users are divided into service user and nonservice user. Therefore, the coherent utility value is suggested to the user evaluation procedure, so the combination of different users experience character is needed to calculate the integrated utility value. Hence, the users experience characteristics are derived by presentation of physical user, behavior of selfish user and character of the user. Consequently, we minimized the content distribution cost and time with crossover-based sine cosine algorithm (CSCA). The proposed CSCA was established for the selection of user service number. The experimental results of proposed method can decrease the multimedia user cost and improve the performance of multimedia content.

Cooperative and efficient content caching and distribution mechanism in 5G network

This paper considers encoding caching and collaborative distribution issues jointly, and proposes a collaborative and efficient content caching and distribution mechanism; and based on the proposed scheme, an energy consumption model is established from the perspective of content caching and content distribution to optimize Energy efficiency of content caching and content distribution. In addition, this paper proposes a cache placement algorithm based on heuristic greedy algorithm to solve this energy efficiency optimization problem by optimizing cache placement. Finally, the simulation results show the performance of the scheme. This paper proposes a content clustering distribution scheme for the problems of random user distribution and limited cache. This solution uses the user’s media cloud and communication technology to complete the distribution of video content. In addition, the use of an efficient adaptive media cloud clustering mechanism and a minimized hops algorithm has enabled the reasonable deployment of 5G network caches based on the popularity of video content and user distribution. The simulation results show that the system content request response time is shortened by 50%, which effectively reduce the impact of content popularity changes on system performance. This paper analyses the important performance indicators such as cache hit rate, optimal communication radius, and maximum link access in 5G networks for multi-layer popular multimedia content distribution in response to the heterogeneity and transmission interference problems for 5G networks in 5G communications. Under the premise of noise ratio and signal-to-interference ratio threshold, related service link selection and scheduling algorithms is further proposed obtaining simulation results that approximate theoretical analysis, which effectively reduces the number of interference by 40% and improves the system throughput by 10%.

Scenario-based cyber attack·defense education system on virtual machines integrated by web technologies for protection of multimedia contents in a network

Security issues have been raised with the tremendous growth of social multimedia content evolving content distribution and social interaction. To protect the social multimedia contents in a network, information security education has been actively studied, and various educational systems are being provided. Even when trainees have completed the training that they can practice, such as DDoS(Distributed Denial of Services) response and database outbreak attack, most of the education methods are based on the theory or advance notification method designed as part of the scenario beforehand and it is difficult to apply these to actual situations. In addition, building new systems and learning to use them for each scenario lowers the training effectiveness. In this paper, we investigate various cases of cyber incident in the content distribution networks. Based on research, we develop Meltdown, Mirai malware, Carbanak APT(Advanced Persistent Threats), and Ransomware scenarios that assume a real multimedia contents distribution situation. We also build and store individual virtual environments for each scenario, integrating them into VMWare ESXI, so that attack and defense practices can be conducted similar to real world networks. The deployed environment is integrated with the Web through the VM API(Virtual Machine Application Programming Interfaces). When the trainee finishes the training, one utilizes the snapshot function of the virtual machine to return the virtual environment to the pre-training state. Trainees do not need to build a virtual environment for each scenario, but they are able to conduct training in an environment similar to a real network by calling a virtual environment with a simple operation from a web browser. Therefore, it is expected that the convenience of trainees and the efficiency of the education will be increased.

Intra mode selection using classical secretary problem (CSP) in high efficiency video coding (HEVC)

In order to support fast distribution of multimedia contents on social media and internet, a fast intra mode decision strategy is proposed in this article to reduce the encoding time of multimedia contents by removing the brute force intra mode selection procedure of HEVC. This article, firstly, improves the performance of the rough-mode-decision (RMD) component of HEVC by constructing the candidate intra mode list by employing a new measure that is fusion of the Hadamard-cost and the statistical-inference formed using spatial/ temporal information. Later, an optimal stopping point prediction model is constructed that outperforms the existing optimal stopping models proposed for HEVC by giving promising balance between the increase in bit-rate and the decrease in complexity. Finally, an early intra mode termination is predicted using the proposed optimal stopping model. Simulation results demonstrate that the proposed model has a wide application and provides early termination for a generic class of decision problems.

CloudWave: Content gathering network with flying clouds

Abstract The necessity of achieving high streaming quality requires to combine the benefits of cloud computing with the verticals of a content delivery network into a robust, reliable, flexible and fault-tolerant system. This paper presents a solution for data acquisition, processing and Internet-enabled distribution of multimedia content. The objective is to propose an elastic content gathering network which handles media files delivered, for instance, by unmanned aerial vehicles, being further served on-demand to end users scattered over the globe. We have implemented the framework architecture, the system components with their attached responsibilities and capabilities, evaluating the performance based on the extensive simulations. The designed framework has been validated in terms of ensured correctness. Experimental results have proven the proper behavior of the built system by handling both types of requests, as for the storage of massive incoming data sets and for distributing content through multiple employed servers strategically placed in the proximity of the initiated requests’ locations. Furthermore, once an autonomous and scalable network has been successfully designed, the number of required surrogate servers dynamically adjusts to consume the multimedia services in a cost-efficient manner.

Secure multimedia distribution in cloud computing using re-encryption and fingerprinting

Recently, cloud computing becomes a main platform for the distribution of multimedia content. The paradigm of multimedia distribution has been shifted from the models in traditional ways to the one in cloud computing. Security and privacy are two most important issues in multimedia distribution. The new model in cloud computing concerns the following issues. Firstly, outsourced content should be confidential except a data owner (DO). Secondly, the CSP is semi-trusted in the public cloud computing environment. A malicious data user (DU) may collude with the CSP to harm the DO’s rights and interests. Thirdly, the rights and interests of DU, including anonymity and unlinkability, should be protected. Based on the above problems, we propose and analyze a Multimedia Distribution based Re-encryption and Fingerprinting (MDRF) scheme in cloud computing. The proposed scheme 1) allows efficient distribution of the content while preserving security and privacy of copyright holders and end users, 2) resolve the problems of piracy tracing, collusion resistance, and dispute resolution, and 3) protect the rights and interests of DU, including anonymity and unlinkability. The analysis part demonstrates that the security of DO and DU are well provided in the proposed scheme. The experimental results evaluate the performance of our framework in terms of collusion resistance of the fingerprint and imperceptibility of fingerprint embedding.

Proactive Caching Placement for Arbitrary Topology With Multi-Hop Forwarding in ICN

With the rapid growth of network traffic and the enhancement of the quality of experiences of users, Information-Centric Networking (ICN), which is a content-centric network architecture with named data caching and routing, is proposed to improve the multimedia content distribution efficiency. In arbitrary topology, cache nodes and users are randomly distributed and connected, hence it is challenging to achieve an optimal caching placement under this situation. In this paper, we propose a caching placement algorithm for arbitrary topology in ICN. We formulate an optimization problem of proactive caching placement for arbitrary topology combined with multi-hop forwarding, with an objective to optimize the user delay and the load balancing level of the nodes simultaneously. Since the original problem is NP-hard, we solve the formulated caching placement problem in two sub-problems, content replica allocation sub-problem and content replica placement sub-problem. First, in the content replica allocation sub-problem, the replica number of each content is obtained by utilizing the auction theory. Second, the replica number of each content is used as a constraint for the content replica placement sub-problem, which is solved by matching theory. The caching placement algorithm combined with multi-hop NRR forwarding maximizes the utilization of cache resources in order to achieve better caching performance. The numerical results show that significant hop count savings and load balancing level improvement are attainable via the proposed algorithm.

Subband CQI Feedback-Based Multicast Resource Allocation in MIMO-OFDMA Networks

Multicast transmission is one of the key enablers toward a more spectral- and energy-efficient distribution of multimedia content in current and envisaged cellular networks. In order to ensure that all users in a multicast group are able to correctly decode the received data, most multicast techniques adopt rather conservative strategies that select a very robust modulation and coding scheme (MCS) whose characteristics are determined by the propagation conditions experienced by the worst user in the group. Obviously, this robustness comes at the prize of a low spectral efficiency. Moreover, in the specific context of wideband communication systems, the selection of the multicast MCS has often relied on the use of wideband channel quality indicators (CQIs) providing rather imprecise information regarding the potential capacity of the multicast channel. Only recently has the per-subband CQI been used to improve the spectral efficiency of the system without compromising the link robustness. However, most subband-CQI multicast schemes proposed so far rely on overpessimistic assumptions that preclude the achievement of high data rates. In this paper, novel subband CQI-based multicast strategies are proposed that, relying on the selection of more spectrally efficient transmission modes, lead to increased data rates while still being able to fulfill prescribed quality of service metrics. To this end, a constrained optimization problem is posed that seeks to maximize the data rate of the whole multicast group while ensuring that the average block error rate for all users remains below a threshold and that a minimum data rate is guaranteed for all users in the group.