Content Distribution Scheme Research Articles

The Evolution of Generative AI: Implications for the Media and Film Industry

Abstract: This research paper scrutinizes and explores the substantial impact of Artificial Intelligence (AI) and Generative AI on the media and film industry. It delves into the continuously evolving applications of AI algorithms and advanced models, emphasizing their profound implications for content creation, production workflows, and distribution strategies. The paper offers comprehensive insights into the operational mechanics of key AI models, underscoring their direct relevance within the domain of media and film. This inquiry provides a timeless and academic perspective on the transformative influence of AI and Generative AI in these industries, facilitating a deeper understanding of their applications and implications

The Evolution of Generative AI: Implications for the Media and Film Industry

This research paper scrutinizes and explores the substantial impact of Artificial Intelligence (AI) and Generative AI on the media and film industry. It delves into the continuously evolving applications of AI algorithms and advanced models, emphasizing their profound implications for content creation, production workflows, and distribution strategies. The paper offers comprehensive insights into the operational mechanics of key AI models, underscoring their direct relevance within the domain of media and film. This inquiry provides a timeless and academic perspective on the transformative influence of AI and Generative AI in these industries, facilitating a deeper understanding of their applications and implications.

Intelligent Content Distribution System: A Machine Learning–Based Teaching Content Customization Method

With the rapid development of information technology and the increasing diversity in the education field, personalized teaching has become the key to educational innovation. However, existing methods of customizing and distributing teaching contents often fail to effectively cope with complex teaching environments and resource constraints. To solve this problem, this study proposed a novel intelligent content distribution system, i.e., a teaching content customization method based on machine learning. First, an inverse optimization model of teaching content customization targeted at content combinations was constructed, which minimized the adjustment of configuration parameters and customized resource constraints. This model generated more reasonable and effective teaching content configuration plans by comprehensively considering content diversity, students’ needs, and resource constraints. Second, to further improve the efficiency and effect of content distribution, an intelligent content distribution algorithm based on a hierarchical reinforcement learning network was developed, which allowed the system to automatically learn and adjust content distribution strategies based on students’ needs and preferences, and optimized the teaching content configuration while meeting resource constraints. The method proposed in this study not only significantly improved the teaching effect, but also provided strong technical support for the rational allocation of educational resources, thereby bringing profound impacts to modern education. This study is of great significance for intelligent content distribution and personalized teaching in the education field.

Unveiling Anime Preferences: A Data-driven Analysis using MyAnimeList API

This research paper presents a data-driven analysis of anime preferences using the MyAnimeList (MAL) API. The objective of this study is to uncover valuable insights into the preferences of anime viewers by analyzing data obtained through MAL API. The paper begins with a comprehensive background that includes a detailed literature survey to understand anime recommendation systems. The methodology employed in this study involves utilizing the MAL API to collect and analyze data related to anime. The API provides access to a wide range of anime related information including ratings, ranks, genres etc. The collected data is then preprocessed to ensure its quality and suitability for analysis, involving data cleaning and transformation. Various data analysis techniques are applied to the preprocessed data. These techniques help identify patterns and trends in different fields such as genres, studios, media type etc. The findings from the data analysis provide valuable insights into the preferences of anime viewers and help to identify features that may be crucial while building the model. Then using cosine similarity model, content-based anime recommendation system is built after feature engineering. The results of this research highlight the importance of leveraging data-driven approaches in understanding and catering to the diverse preferences of anime viewers. By EDA uncovering patterns and trends in anime preferences, anime recommendation systems can be enhanced to provide more relevant recommendations to viewers. The implications of this research extend to the anime industry, where a deeper understanding of viewer preferences can inform content production and distribution strategies. In conclusion, this research paper presents a data-driven analysis and content-based recommendation system for anime using the MAL API and data science. The methodology employed involves data collection, preprocessing, Exploratory Data Analysis(EDA), Feature engineering and Model building. The findings contribute to enhancing anime recommendation systems and understanding the diverse preferences of anime viewers.

Designing personalisation of European public service media (PSM): trends on algorithms and artificial intelligence for content distribution

The migration of audiences to digital environments has motivated the media to develop a content distribution strategy that has a presence in these new spaces. In the case of European public broadcasters, they have strengthened their digital news services and have built video-on-demand platforms where they organise and screen their products. Even so, the overload of information and content reaching users forces corporations to look for new mechanisms to present an adequate, interesting and diverse offering to each of their followers. This research project analyses the use of artificial intelligence in the recommendation systems implemented by 14 European public broadcasters in Germany (ARD and ZDF), Belgium (VRT and RTBF), Denmark (DR), Spain (RTVE), Finland (YLE), France (France TV), Great Britain (BBC), the Netherlands (NPO), Ireland (RTÉ), Italy (RAI), Sweden (SVT) and Switzerland (RTS). The results reveal that there is no unanimity among the corporations with regard to the operation and origin of these systems, which vary between home-made developments, acquired from third parties, or collaborative solutions. Operators differentiate between news recommendation processes and those executed on their VoD platforms and aim to distance their systems from those of commercial media, for which they have already started working on a public service media (PSM) algorithm that includes traditional public media values, avoids filter bubbles, and pays special attention to the European General Data Protection Regulation (GDPR).

A Multi-Agent Deep Reinforcement Learning-Based Popular Content Distribution Scheme in Vehicular Networks.

The Internet of Vehicles (IoV) enables vehicular data services and applications through vehicle-to-everything (V2X) communications. One of the key services provided by IoV is popular content distribution (PCD), which aims to quickly deliver popular content that most vehicles request. However, it is challenging for vehicles to receive the complete popular content from roadside units (RSUs) due to their mobility and the RSUs' constrained coverage. The collaboration of vehicles via vehicle-to-vehicle (V2V) communications is an effective solution to assist more vehicles to obtain the entire popular content at a lower time cost. To this end, we propose a multi-agent deep reinforcement learning (MADRL)-based popular content distribution scheme in vehicular networks, where each vehicle deploys an MADRL agent that learns to choose the appropriate data transmission policy. To reduce the complexity of the MADRL-based algorithm, a vehicle clustering algorithm based on spectral clustering is provided to divide all vehicles in the V2V phase into groups, so that only vehicles within the same group exchange data. Then the multi-agent proximal policy optimization (MAPPO) algorithm is used to train the agent. We introduce the self-attention mechanism when constructing the neural network for the MADRL to help the agent accurately represent the environment and make decisions. Furthermore, the invalid action masking technique is utilized to prevent the agent from taking invalid actions, accelerating the training process of the agent. Finally, experimental results are shown and a comprehensive comparison is provided, which demonstrates that our MADRL-PCD scheme outperforms both the coalition game-based scheme and the greedy strategy-based scheme, achieving a higher PCD efficiency and a lower transmission delay.

QoE-Aware Efficient Content Distribution Scheme For Satellite-Terrestrial Networks

The satellite-terrestrial networks (STN) utilize the spacious coverage and low transmission latency of the Low Earth Orbit (LEO) constellation to transfer requested content for subscribers especially in remote areas. With the development of storage and computing capacity of satellite onboard equipment, it is considered promising to leverage in-network caching technology on STN to improve content distribution efficiency. However, traditional caching and distribution schemes are not suitable in STN, considering dynamic satellite propagation links and time-varying topology. More specifically, the unevenness of user distribution heightens difficulties for assurance of user quality of experience. To address these problems, we first propose a density-based network division algorithm. The STN is divided into a series of blocks with different sizes to amortize the data delivery costs. To deploy the caching satellites, we analyze the link connectivity and propose an approximate minimum coverage vertex set algorithm. Then, a novel cache node selection algorithm is designed for optimal subscriber matching. On the basis of time-varying network model, the STN cache content updating mechanism is derived to enable a stable and sustainable quality of user experience. The simulation results demonstrate that the proposed user-oriented STN content distribution scheme can obviously reduce the average propagation delay and network load under different network conditions and has better stability and self-adaptability under continuous time variation.

Granular Content Distribution for IoT Remote Sensing Data Supporting Privacy Preservation

Facilitated by the Internet of Things (IoT) and diverse IoT devices, remote sensing data are evolving into the multimedia era with an expanding data scale. Massive remote sensing data are collected by IoT devices to monitor environments and human activities. Because IoT devices are involved in the data collection, there are probably private data contained in the collected remote sensing data, such as the device owner information and the precise location. Therefore, when data analysts, researchers, and other stakeholders require remote sensing data from numerous IoT devices for different analyses and investigations, how to distribute massive remote sensing data efficiently and regulate different people to view different parts of the distributed remote sensing data is a challenge to be addressed. Many general solutions rely on granular access control for content distribution but do not consider the low computational efficiency caused by the huge file size of the remote sensing data or certain IoT devices only have a constrained computational performance. Therefore, we propose a new granular content distribution scheme, which is more lightweight and practical for the distribution of multimedia remote sensing data with the consideration of the large data size to avoid complicated operations to the data. Furthermore, a dual data integrity check (hash summary and watermark) designed in our scheme can detect tampering or forgery from encrypted remote sensing data before decrypting it and validate it again after decryption. The security analyses and experimental results manifest that our new scheme can maintain high computational efficiency and block tampering and forgery during the granular content distribution for IoT remote sensing data.

Analysis of Social Media Algorithm Recommendation System

Once, the Internet was considered an important force to promote social public welfare activities and spread social justice. Today, the driving force of the Internet on the spread of negative social information and polarisation also exists. Social media platforms use this kind of method to customise personalised content distribution schemes by mining and analysing individual historical behaviour data based on algorithms. This behaviour of the platform brings about efficient and accurate information matching. At the same time, the content recommended by the algorithm is also controversial due to the risk of privacy violations and other issues. This article will analyse the advantages and disadvantages of social media platform algorithms and recommendation systems for citizens.

SECURE AND ENERGY-AWARE ROUTING FOR RELIABLE DATA TRANSMISSION IN MOBILE AD HOC NETWORKS

Mobile Ad-Hoc Network (MANET) is a generic technology utilized in various real-time applications. The MANET is a self-configuring and self-directed system that quickly deploys in a network. Data transfer is a big issue in MANET due to poor wireless medium and lack of data security and reliability. The prior system established a mobile ad hoc content distribution strategy. The ideal Cluster Head (CH) is chosen using the Teaching Learning Based Optimization (TLBO) approach. However, due to these features, it is more vulnerable to attacks. In addition, it impacts packet delivery and throughput. The proposed system designed a Secure and Energy Aware Routing (SEAR) mechanism to improve MANET data transmission reliability and security. Initially, nodes are clustered by distance. Then the Weight-based Artificial Fish Swarm Algorithm (WAFSA) is used to pick the optimum Cluster Head (CH). Mobility, remaining energy, and connectivity are considered objective functions for CH selection. Securing communication between these nodes will require proper cryptography. The security mechanism generates secret keys using Advanced Encryption Standard (AES) to encrypt or decode messages delivered between member nodes. This security approach enables secure communication between member nodes and CHs, uses the NS-2 analytical platform, and outperforms the existing system regarding average energy drop, network lifetime, and encryption time.

Content-distribution strategies in markets with locked-in customers

We study how the presence of locked-in customers in a downstream market affects the distribution choice of an upstream content provider. Two asymmetric distributors compete in a mature market and the content provider sells its rights using lump-sum fees. A higher number of locked-in customers reduces the need to resort to exclusivity to relax downstream competition. The content provider therefore sells its rights to both distributors when there is a sufficiently-high proportion of locked-in customers. We show that an exclusive affiliation with the smaller rather than the larger distributor facilitates distributors’ rent-extraction, in particular for low-quality content. When there are few locked-in customers, the content provider sells low-quality content to the smaller distributor and high-quality content to the larger distributor. Our results suggest that competition authorities should cautiously evaluate the effects of lower switching costs on consumer welfare. By encouraging exclusive distribution, a lower proportion of locked-in customers may reduce consumer welfare.

Secure Content Distribution with Access Control Enforcement in Named Data Networking.

NDN is one of the new emerging future internet architectures which brings up new solutions over today’s internet architecture, facilitating content distribution, in-network caching, mobility support, and multicast forwarding. NDNs ubiquitous in-network caching allows consumers to access data directly from the intermediate router’s cache. However, it opens content privacy problems since data packets replicated in the router are always accessible by every consumer. Sensitive contents in the routers should be protected and accessed only by authorized consumers. Although the content protection problem can be solved by applying an encryption-based access control policy, it still needs an efficient content distribution scheme with lower computational overhead and content retrieval time. We propose an efficient and secure content distribution (ES_CD), by combining symmetric encryption and identity-based proxy re-encryption. The analysis shows that our proposed scheme achieves content retrieval time reduction up to 20% for the cached contents in our network simulation environment and a slight computational overhead of less than 19 ms at the content producer and 9 ms at the consumer for 2 KB content. ES_CD provides content confidentiality and ensures only legitimate consumers can access the contents during a predefined time without requiring a trusted third party and keeping the content producer always online.

Caching Policies for Delay Minimization in Small Cell Networks With Coordinated Multi-Point Joint Transmissions

In 5G and beyond network architectures, operators and content providers base their content distribution strategies on Heterogeneous Networks, where macro and small cells are combined to offer better Quality of Service to wireless users. On top of such networks, edge caching and Coordinated Multi-Point (CoMP) joint transmissions are used to further improve performance. In this paper, we address the average delay minimization problem by first formulating it as a static optimization problem. Even though the problem is NP-hard we are able to solve it via an efficient algorithm that guarantees a 1/2-approximation ratio. We then proceed to propose two fully distributed and dynamic caching policies for the same problem. The first one asymptotically converges to the static optimal solution under the Independent Reference Model (IRM). The second one provides better results in practice under real (non-stationary) request processes. Our online policies outperform existing dynamic solutions that are PHY-unaware.

A Machine Learning Approach to Enhance the Performance of D2D-Enabled Clustered Networks

Clustering has been suggested as an effective technique to enhance the performance of multicasting networks. Typically, a cluster head is selected to broadcast the cached content to its cluster members utilizing Device-to-Device (D2D) communication. However, some users can attain better performance by being connected with the Evolved Node B (eNB) rather than being in the clusters. In this article, we apply machine learning algorithms, namely Support Vector Machine, Random Forest, and Deep Neural Network to identify the users that should be serviced by the eNB. We therefore propose a mixed-mode content distribution scheme where the cluster heads and eNB service the two segregated groups of users to improve the performance of existing clustering schemes. A D2D-enabled multicasting scenario has been set up to perform a comprehensive simulation study that demonstrates that by utilizing the mixed-mode scheme, the performance of individual users, as well as the whole network, improve significantly in terms of throughput, energy consumption, and fairness. This study also demonstrates the trade-off between eNB loading and performance improvement for various parameters.

The Opium of the Paquete: State Censorship, Private Self-Censorship, and the Content-Distribution Strategies of Cuba's Emergent, Independent Digital-Media Start-ups

The Opium of the Paquete: State Censorship, Private Self-Censorship, and the Content-Distribution Strategies of Cuba's Emergent, Independent Digital-Media Start-ups

Joint optimization strategy for QoE-aware encrypted video caching and content distributing in multi-edge collaborative computing environment

The video request service of users in 5G network will explode, and adaptive bit rate technology can provide users with reliable video response. Placing video resources on edge servers close to users can overcome the problem of excessive network load similar to traditional centralized cloud platform solutions. Moreover, multiple edge servers can provide caching and transcoding support by collaboration mechanisms, which further improves users’ Quality of Experience (QoE). However, the design difficulty of video caching and content distribution strategies is increased due to the diversity of collaboration mechanisms and the competition between local and collaborative services of edge servers for computing and storage resources. In order to solve this problem, video cache and content distribution problem is modeled as random integer programming problem in the multi-edge server at most two-hop collaboration scenario. In order to improve the security of video data transmission, the video stream is encrypted using an encryption algorithm based on Logistic chaotic-Quantum-dot Cellular Automata (QCA). For improving the efficiency of solving integer programming problems, this paper uses a pyramid intelligent evolution algorithm based on optimal cooperation strategy to solve this problem. Simulation experiments show that our proposed method can obtain higher QoE value compared with several newer methods. In addition, the average access delay of proposed method is shortened by more than 27.98%, which verifies its reliability.

A Novel Weighted Clustering Algorithm Supported by a Distributed Architecture for D2D Enabled Content-Centric Networks.

Next generation cellular systems need efficient content-distribution schemes. Content-sharing via Device-to-Device (D2D) clustered networks has emerged as a popular approach for alleviating the burden on the cellular network. In this article, we utilize Content-Centric Networking and Network Virtualization to propose a distributed architecture, that supports efficient content delivery. We propose to use clustering at the user level for content-distribution. A weighted multifactor clustering algorithm is proposed for grouping the D2D User Equipment (DUEs) sharing a common interest. The proposed algorithm is evaluated in terms of energy efficiency, area spectral efficiency, and throughput. The effect of the number of clusters on these performance parameters is also discussed. The proposed algorithm has been further modified to allow for a tradeoff between fairness and other performance parameters. A comprehensive simulation study demonstrates that the proposed clustering algorithm is more flexible and outperforms several classical and state-of-the-art algorithms.

EdgeVCD: Intelligent Algorithm-Inspired Content Distribution in Vehicular Edge Computing Network

Vehicular edge computing (VEC), which integrates mobile-edge computing (MEC) into vehicular networks, can provide more capability for executing resource-hungry applications and lower latency for connected vehicles. Distributing the result content to connected vehicles is vital for them to take proper actions based on computing results. However, the increasing number of connected vehicles and the limited communication resources make the content distribution a challenge. Besides, the diversity of connected vehicles and contents makes it more challenging for content distribution. To address this issue, in this article, we propose EdgeVCD, an intelligent algorithm-inspired content distribution scheme. Specifically, we first propose a dual-importance (DI) evaluation approach to reflect the relationship between the Priority of Vehicles (PoV) and the Priority of Contents (PoC). To make use of the limited communication resources, we then formulate an optimization problem to maximize the system utility for content distribution. To solve the complex optimization problem effectively, we first divide the road into small segments. Then, we propose a fuzzy-logic-based method to select the most proper content replica vehicle (CRV) for aiding content distribution and redefine the number of content request vehicles in each segment. Thereafter, the optimization problem is transformed into a nonlinear integer programming problem. Inspired by the artificial immune system, we propose an immune clone-based algorithm to solve it, which has a fast convergence to an optimal solution. Extensive simulations validate the effectiveness of our proposed EdgeVCD in terms of system utility, average utility, and convergence.

Evaluation of Content Dissemination Strategies in Urban Vehicular Networks

The main drivers for the continuous development of Vehicular ad-hoc Networks (VANETs) are safety applications and services. However, in recent years, new interests have emerged regarding the introduction of new applications and services for non-urgent content (e.g., videos, ads, sensing and touristic information) dissemination. However, there is a lack of real studies considering content dissemination strategies to understand when and to whom the content should be disseminated using real vehicular traces gathered from real vehicular networks. This work presents a realistic study of strategies for dissemination of non-urgent content with the main goal of improving content delivery as well as minimizing network congestion and resource usage. First, we perform an exhaustive network characterization. Then, several content strategies are specified and evaluated in different scenarios (city center and parking lot). All the obtained results show that there are two content distribution strategies that clearly set themselves apart due to their superior performance: Local Rarest Bundle First and Local Rarest Generation First.

An Efficient Framework for Network Code based Multimedia Content Distribution in Hybrid P2P Network

Most of the existing peer-to-peer (P2P) content distribution schemes carry out a random or rarest piece first content dissemination procedure to avoid duplicate transmission of the same pieces of data and rare pieces of data occurring in the network. This problem is solved using P2P content distribution based on network coding scheme. Network coding scheme uses random linear combination of coded pieces. Hence, the above-stated problem is solved easily and simply. Our proposed mechanism uses network coding mechanism in which several contents having the same message are grouped into different groups and coding operation is performed only within the same group. The interested peers are also divided into several groups with each group having the responsibility to spread one set of contents of messages. The coding system is designed to assure the property that any subset of the messages can be utilized to decode the original content as long as the size of the subset is suitably large. To meet this condition, dynamic smart network coding (DSNC) scheme is defined which assures the preferred property, then peers are connected in the same group to send the corresponding message, and connect peers in different groups to disseminate messages for carrying out decoding operation. Moreover, the proposed system is readily expanded to support topology change to get better system performance further in terms of reliability, link stress and throughput. The simulation results prove that the proposed system can attain 20–25% higher throughput than existing systems, good reliability, link failure and robustness to peer churn.