Content Delivery Framework Research Articles

Enhancing the driving experience of smart city users based on content delivery framework for intelligent transportation systems

Integration of several communication technologies that facilitate user access contributes to the rapid development of the smart city notion. Intelligent transportation systems (ITS) are introduced as part of smart city development to provide drivers with enhanced communication and information-sharing capabilities. The article introduces a novel ITS content delivery framework (CDF) that addresses communication outage issues. CDF-ITS uses End-to-end decision-making system modelling to examine factors such as communication, content distribution, and vehicle features. A suitable communication slot for vehicular users is determined by processing these characteristics based on outage time and variables. By allocating time-aware communication slots according to the classification of the propagation factor, outage problems may be reduced. End-to-end decision-making is used for classification and vehicle attribute balance, allowing immediate responses to user requests. The experimental outcomes show that the latency of 0.297 s, outage time of 0.0837, distributed messages of 276, and computing complexity of 11.87 are used to assess the proposed framework's efficiency across vehicle density and velocities.

A Vehicle–Road Urban Sensing Framework for Collaborative Content Delivery in Freeway-Oriented Vehicular Networks

A Vehicle–Road Urban Sensing Framework for Collaborative Content Delivery in Freeway-Oriented Vehicular Networks

Deep Adversarial Reinforcement Learning based Incentive Mechanism for Content Delivery in D2D-Enabled Mobile Networks

With the explosive growth of mobile devices at the edge of mobile networks, the amount of content that needs to be transmitted is exploded. As it takes resources (cache, bandwidth, and power) for the edge server and nearby devices to deliver content, the incentive mechanism with the optimal pricing and content delivery strategy needs to be studied without relying on the knowledge of network parameters in a competitive environment. In this paper, built on the framework of content delivery with the Stackelberg game, we have proposed a robust deep reinforcement learning method based on adversarial training to learn price strategy for lack of knowledge of private information. Specifically, the Stackelberg game is constructed to describe the interaction between different service providers and service demanders, and a deep adversarial reinforcement learning method is proposed to approximate the opponent’s decision in the worst case, where the price strategy can be trained end-to-end in the competitive environment. The simulation results show that the proposed algorithm can form an effective price strategy and improve the utility of service providers.

Digital twin driven and intelligence enabled content delivery in end-edge-cloud collaborative 5G networks

The rapid development of 5G/6G and AI enables an environment of Internet of Everything (IoE) which can support millions of connected mobile devices and applications to operate smoothly at high speed and low delay. However, these massive devices will lead to explosive traffic growth, which in turn cause great burden for the data transmission and content delivery. This challenge can be eased by sinking some critical content from cloud to edge. In this case, how to determine the critical content, where to sink and how to access the content correctly and efficiently become new challenges. This work focuses on establishing a highly efficient content delivery framework in the IoE environment. In particular, the IoE environment is re-constructed as an end-edge-cloud collaborative system, in which the concept of digital twin is applied to promote the collaboration. Based on the digital asset obtained by digital twin from end users, a content popularity prediction scheme is firstly proposed to decide the critical content by using the Temporal Pattern Attention (TPA) enabled Long Short-Term Memory (LSTM) model. Then, the prediction results are input for the proposed caching scheme to decide where to sink the critical content by using the Reinforce Learning (RL) technology. Finally, a collaborative routing scheme is proposed to determine the way to access the content with the objective of minimizing overhead. The experimental results indicate that the proposed schemes outperform the state-of-the-art benchmarks in terms of the caching hit rate, the average throughput, the successful content delivery rate and the average routing overhead.

Edge Computing-Based Layered Video Streaming Over Integrated Satellite and Terrestrial 5G Networks

In the coming era of telecommunication, the integration of satellite capabilities with emerging 5G technologies has been considered as a promising solution to achieve assured user experiences in bandwidth-hungry content applications. In this paper, we present our design for emerging Multi-access Edge Computing (MEC) based Video on Demand (VoD) services, which efficiently utilizes satellite and terrestrial integrated 5G network. Based on this framework, we propose and analyse the Video-segment Scheduling Network Function (VSNF), which is able to deliver enhanced quality of video consumption experiences to end-users. We specifically consider the layer video scenario, where it is possible to intelligently schedule layers of video segments via parallel satellite and terrestrial backhaul links in 5G. The key technical challenge is to optimally schedule the layered video segment over the two network link which are having distinct characteristics while attempting to enhance the Quality of Experience (QoE) for all the end-users in fair manner. We have conducted extensive set of experiments using real 5G testing framework in which gNB is integrated with core network using Geostationary Earth Orbit (GEO) satellite and terrestrial backhaul links. The results highlights the capability of our proposed content delivery framework for holistically delivering assured QoE, fairness among multiple video sessions, as well as optimised network resource efficiency.

CROWD-CDN: A cryptocurrency incentivized crowdsourced peer-to-peer content delivery framework

Content Distribution Network (CDN) is one of the most established technology for improving application and media service providers’ scalability, quality of service (QoS), and quality of experience (QoE). A hybrid content delivery network (CDN) with peer-to-peer (P2P) networking (CDN-P2P) is one strategy for further improving the scalability and QoS/QoE. However, CDN-P2P suffers from peer churning and free riding due to the inexistence of stimulating monetary incentives for the participating peers in the P2P overlay. Furthermore, to incentivize participating peers according to the peer contributions, it is not clear how much a peer contributes back to the overall content distribution capacity. To this end, this manuscript proposes a cryptocurrency incentivized crowdsourced P2P content delivery network (CROWD-CDN). CROWD-CDN monetizes the services of seeds supplying peers according to their contribution to the overall CDN content distribution capacity. CROWD-CDN employed blockchain-based cryptocurrencies and smart contracts for the monetization of peers. Additionally, CROWD-CDN utilizes the container-based virtualization of the peer nodes enabling CDN providers for transparent monitoring of peers’ contribution and securing the CDN contents at the peer in the isolated container. Lastly, we provide a proof of concept simulated testbed for the dimensioning of different variants of the proposed hybrid architecture and validating the provisioning of monetary incentives to peers.

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.

A Stackelberg Game-Based Caching Incentive Scheme for Roadside Units in VANETs.

As a key technology of intelligent transportation systems (ITS), vehicular ad hoc networks (VANETs) have been promising to provide safety and infotainment for drivers and passengers. To support different applications about traffic safety, traffic efficiency, autonomous driving and entertainment, it is important to investigate how to effectively deliver content in VANETs. Since it takes resources such as bandwidth and power for base stations (BSs) or roadside units (RSUs) to deliver content, the optimal pricing strategy for BSs and the optimal caching incentive scheme for RSUs need to be studied. In this paper, a framework of content delivery is proposed first, where each moving vehicle can obtain small-volume content files from either the nearest BS or the nearest RSU according to the competition among them. Then, the profit models for both BSs and RSUs are established based on stochastic geometry and point processes theory. Next, a caching incentive scheme for RSUs based on Stackelberg game is proposed, where both competition sides (i.e., BSs and RSUs) can maximize their own profits. Besides, a backward introduction method is introduced to solve the Stackelberg equilibrium. Finally, the simulation results demonstrate that BSs can obtain their own optimal pricing strategy for maximizing the profit as well as RSUs can obtain the optimal caching scheme with the maximum profit during the content delivery.

Joint Distributed Link Scheduling and Power Allocation for Content Delivery in Wireless Caching Networks

In wireless caching networks, the design of the content delivery method must consider random user requests, caching states, network topology, and interference management. In this article, we establish a general framework for content delivery in wireless caching networks without stringent assumptions that restrict the network structure and interference model. Based on the framework, we propose a dynamic and distributed link scheduling and power allocation scheme for content delivery that is assisted by belief-propagation (BP) algorithms. The proposed scheme achieves three critical purposes of wireless caching networks: 1) limiting the delay of user request satisfactions, 2) maintaining the power efficiency of caching nodes, and 3) managing interference among users. In addition, we address the intrinsic problem of the BP algorithm in our network model, proposing a matching algorithm for one-to-one link scheduling. Simulation results show that the proposed scheme provides almost the same delay performance as the optimal scheme found through an exhaustive search at the expense of a little additional power consumption and does not require a clustering method and orthogonal resources in a large-scale D2D network.

QoE Analysis in Cache-Enabled Multi-UAV Networks

This paper presents a quality of experience (QoE)-guided content delivery framework in cache-enabled multi-unmanned aerial vehicle (UAV) networks, measured from the content delay index (CDI), which is formulated based on the latency involved in the delivery of contents requested by ground-based mobile users. The mobile users use the cellular devices to raise the content requests. Additionally, multiple UAVs act as aerial base stations to deliver the requested contents to ground-based mobile users. The proposed work aims to maximize the QoE. Additionally, the existing works do not consider the necessity of creating groups of ground-based mobile users based on geographic locations in order to provide contents seamlessly. Therefore, we construct an optimization problem to maximize the average CDI, which is proved as an NP-hard problem. Further, multiple UAVs incur the challenges of placing the UAVs in 3D space over the distributed ground-based mobile users. To solve this NP-hard problem, we propose QoE analysis in cache-enabled multi-UAV networks (QMUN). It functions in three steps. First, it creates clusters for the ground-based mobile users using the K-means algorithm and finds the optimized 2D positions of all the UAVs. Second, it finds the optimized heights of all UAVs before assigning them to the individual cluster. Finally, it infuses proactive content caching and optimizes the cache policy. Therefore, the delay associated with the content delivery is minimized, which leads to the generation of high value of CDI. The results of simulation depict the usefulness of QMUN in terms of average CDI, average throughput, average end-to-end delay, and user satisfaction. Specifically, the results depict that the average CDI of each user improves by $11.04$%, $4.46$%, and $17.88$% compared to the QMUN Without Cache (QMUN-WC), Greedy, and Random Selection (RS).

Content Caching and Distribution at Wireless Mobile Edge

Mobile edge computing can provision hierarchical cloud resources at the edge, and efficiently reduce the distance that the remote data need to travel towards the end-users. To empower content delivery at the edge and ultimately transform “shorter distance” to “less time”, instead of focusing only on the storage aspects (e.g., allocation of limited media caches) of mobile edge, the radio aspects (e.g., interference models caused by sharing limited RBs) should be jointly considered. We hence propose a novel comprehensive analytical content caching and delivery framework for the cloud enhanced mobile edge with hierarchical radio access points. To investigate the impacts of limited radio resources (e.g., power, spectrum, and time) and storage resources, we propose a joint user scheduling and caching (JSC) scheme to optimize the end to end performance in terms of throughput (i.e., the number of successful content requests). By leveraging the backhaul links among multi-tier access points, the proposed algorithm can tap on the potential of the coded multi-casting scheme, which is designed to reduce the traffic load among the network. Simulation results demonstrate that the JSC algorithm can improve the resource utilization efficiency and provide a significant sum throughput gain.

Using the Flipped Classroom Model in a Junior Level Course to Increase Student Learning and Success

AbstractEngineering courses typically follow a similar framework for content delivery: preread, lecture, and homework. Innovations are starting to be embraced that increase engagement, student lear...

ECDU: an edge content delivery and update framework in Mobile edge computing

Content delivery network (CDN) has gained increasing popularity in recent years for facilitating content delivery. Most existing CDN-based works upload the content generated by mobile users to the cloud data center firstly. Then, the cloud data center delivers the content to the proxy server. Finally, the mobile users request the required content from the proxy server. However, uploading all the collected content to the cloud data center increases the pressure on the core network. In addition, it also wastes a lot of bandwidth resources because most of the content does not have to be uploaded. To make up for the shortcomings of existing CDN-based works, this article proposes an edge content delivery and update (ECDU) framework based on mobile edge computing architecture. In the ECDU framework, we deploy a number of content servers to store raw content collected from mobile users, and cache pools to store content that frequently requested at the edge of the network. Thus, it is not necessary to upload all content collected by mobile users to the cloud data center, thereby alleviating the pressure of the core network. Based on content popularity and cache pool ranking, we also propose edge content delivery (ECD) and edge content update (ECU) schemes. The ECD scheme is to deliver content from cloud data center to cache pool, and the ECU scheme is to mitigate the content to appropriate cache pools in terms of its request frequency and cache pool ranking. Finally, a representative case study is provided and several open research issues are discussed.

Toward Dynamic Link Utilization for Efficient Vehicular Edge Content Distribution

With the significant advance of connected vehicles, future demand for vehicular infotainment services will be greatly increased. Traditional content distribution approaches based on typical cellular architecture suffer from long latency and unstable connections in high-dynamic vehicular environments and even may cause congestion on the backhaul network due to a large amount of requested data. In this paper, we propose a novel content delivery framework by leveraging the 5G edge networks, in which the content caching and data prefetching techniques are exploited accordingly. We investigate the comprehensive dynamic link utilization problem in 5G edge networks from the perspectives of vehicle users and network operator, respectively. For the vehicle users’ perspective, our aim is to maximize the vehicular content distribution throughput through optimal scheduling of vehicular access link slots, and also the utilization problem of backhaul link slots in edge networks is studied to reduce the data access delay of vehicles. For the network operator's perspective, the objective is to maximize the total profit of the network operator, and therefore, the auction model is utilized for the vehicular access link slot scheduling and the backhaul link utilization is analyzed in terms of compensations and costs. Finally, extensive simulations are conducted to demonstrate the efficiency of the proposed solutions for edge content delivery of connected vehicles.

Social Media Communications and Marketing Strategy: A Taxonomical Review of Potential Explanatory Approaches

The growth of social media has captured the attention of companies, which have responded by increasing their advertising efforts toward social media users. Previous studies have devoted considerable effort to developing social media marketing strategies, but minimal effort has focused on examining the types of messages presented on firms’ social media platforms to communicate with customers. Practitioners face the challenge of identifying which types of social media message content to choose. In addressing this challenge, this article provides a comprehensive framework of social media content strategy, delivery, and impact on the consumers, including a taxonomical approach that helps firms’ understanding of firm-generated content on social media platforms.

A Q-Learning Based Scheme to Securely Cache Content in Edge-Enabled Heterogeneous Networks

With the increasing demands for mobile users to obtain content, the heterogeneous networks (HetNets), which consolidate the backbone networks with mobile edge networks, have been regarded as a promising paradigm to provide mobile users with high quality of experience (QoE). However, the limited network resources and caching capacity become a new challenge to deliver content in HetNets. Therefore, in this paper, a cooperative scheme between edge server and content provider in HetNets is proposed to improve the performance of content delivery. Firstly, a novel framework of content delivery with backbone networks and mobile edge networks is introduced. The edge servers are deployed at the edge of networks and close to users. Secondly, a Q-learning based scheme for content caching is developed to securely cache contents with the cooperation between edge server and content provider. Thirdly, the cooperative interaction between edge server and content provider is modeled as the double auction game. Each player can obtain the maximum utility from the equilibrium strategy. Finally, simulation results show that the proposed scheme can improve the efficiency of content delivery and bring more utilities to edge server and content provider than the conventional schemes.

Femtocaching assisted multi-source D2D content delivery in cellular networks

The influxes of diversified services and mass data lead to exponential growth of traffic load in mobile cellular networks. Cache-enabled device-to-device (D2D) communication provides a general framework to alleviate this situation. In contrast to previous single-source D2D models, this paper investigates a comprehensive content delivery framework based on a three-tier heterogeneous network (HetNet), where base station (BS), femtocaching auxiliary equipments (FAEs), and user terminals(UTs) are included. The cooperative D2D communication can be implemented from both FAEs and UTs to handle the ongoing explosive increase in ultra-dense scenario. Moreover, duplicate storage for requesting data in multiple neighbor nodes makes many-to-one D2D communication possible at the user layer. Considering the case that cellular users and D2D links reuse the same resources in the uplink period, the non-outage probability of the cellular communication is defined to guarantee the main communication quality. Under the constraints subject to cumulative interference, an optimization objective function based on multi-source D2D communication is deduced to achieve unprecedented average data rate. Numerical simulations show that our system yields network throughput exponentially while transferring traffic load of the BS reasonably.

A Game Theoretic Approach to Parked Vehicle Assisted Content Delivery in Vehicular Ad Hoc Networks

Recently, parked vehicles have been shown to be useful to deliver content in vehicular ad hoc networks, where the parked vehicles can form social communities to share and exchange content with other moving vehicles and road side units (RSUs). However, as it takes resource such as bandwidth and power for parked vehicles and RSUs to deliver content, the incentive scheme with the optimal pricing strategy needs to be studied. Furthermore, because multiple places including RSUs and parked vehicles can deliver content to moving vehicles, the optimal algorithm to determine where to obtain the requested content should also be discussed. Therefore, in this paper, we first propose a framework of content delivery with parked vehicles, where moving vehicles can obtain content from both the RSU and parked vehicles according to the competition and cooperation among them. Then, based on a Stackelberg game, we develop a pricing model where each of the three players, including moving vehicles, RSU, and parked vehicles, can obtain their maximum utilities. Next, a gradient based iteration algorithm is presented to obtain the Stackelberg equilibrium. Finally, the simulation results prove that the proposal can outperform other conventional methods and that each player in the game can obtain its optimal strategy during the content delivery.

Modeling, Analysis, and Optimization of Coded Caching in Small-Cell Networks

Coded caching is able to exploit accumulated cache size and hence superior to uncoded caching by distributing different fractions of a file in different nodes. This paper investigates coded caching in a large-scale small-cell network (SCN) where the locations of small base stations (SBSs) are modeled by stochastic geometry. We first propose a content delivery framework, where multiple SBSs that cache different coded packets of a desired file transmit concurrently upon a user request and the user decodes the signals using successive interference cancellation (SIC). We characterize the performance of coded caching by two performance metrics, average fractional offloaded traffic (AFOT) and average ergodic rate (AER), for which a closed-form expression and a tractable expression are derived, respectively, in the high signal-to-noise ratio region. We then formulate the coded cache placement problem for AFOT maximization as a multiple-choice knapsack problem (MCKP). By utilizing the analytical properties of AFOT, a greedy but optimal algorithm is proposed. We also consider the coded cache placement problem for AER maximization. By converting this problem into a standard MCKP, a heuristic algorithm is proposed. Analytical and numerical results reveal several design and performance insights of coded caching in conjunction with SIC receiver in interference-limited SCNs.

Research on the Key Technologies of Base Station Server Content Delivery Based on Broadcast-Storage Architecture

Focusing on Internet’s emerging paradigm of ubiquitous content sharing, following the Broadcast-Storage concept, we study the key technologies of base station server content delivery based on broadcast-storage architecture, introduce a general architecture model of broadcast-storage network, propose a technological framework of base station server content delivery based on broadcast-storage architecture, analyze the key technologies and application algorithms included in the foundation technology layer and the key technology layer. In the key technology layer, we mainly research on Satellite-Base Station data processing technology, base station content service platform, content security management-control, topic security management-control, and user management. Finally, we summarize the nonfunctional requirements of base station service platform. Based on the above research, a base station server content delivery system based on broadcast-storage architecture is designed and implemented, which is docked with the integrated broadcast control platform, integrated management-control platform and clients, completing application demonstration in important typical business. Therefore, a complete broadcast control content delivery system has been initially established, laying solid foundation for further researches.