Video Caching, Analytics and Delivery at the Wireless Edge: A Survey and Future Directions

Future wireless networks will provide high bandwidth, low-latency, and ultra-reliable Internet connectivity to meet the requirements of different applications, ranging from mobile broadband to the Internet of Things. To this aim, mobile edge caching, computing, and communication (edge-C3) have emerged to bring network resources (i.e., bandwidth, storage, and computing) closer to end users. Edge-C3 allows improving the network resource utilization as well as the quality of experience (QoE) of end users. Recently, several video-oriented mobile applications (e.g., live content sharing, gaming, and augmented reality) have leveraged edge-C3 in diverse scenarios involving video streaming in both the downlink and the uplink. Hence, a large number of recent works have studied the implications of video analysis and streaming through edge-C3. This article presents an in-depth survey on video edge-C3 challenges and state-of-the-art solutions in next-generation wireless and mobile networks. Specifically, it includes: a tutorial on video streaming in mobile networks (e.g., video encoding and adaptive bitrate streaming); an overview of mobile network architectures, enabling technologies, and applications for video edge-C3; video edge computing and analytics in uplink scenarios (e.g., architectures, analytics, and applications); and video edge caching, computing and communication methods in downlink scenarios (e.g., collaborative, popularity-based, and context-aware). A new taxonomy for video edge-C3 is proposed and the major contributions of recent studies are first highlighted and then systematically compared. Finally, several open problems and key challenges for future research are outlined

Auction-based Cache Trading for Scalable Videos in Multi-Provider Heterogeneous Networks

Content providers (CPs) are keen to cache their popular contents in small-cell base stations (SBSs) provided by mobile network operators (MNOs). In fact, they can serve the requests of their subscribers with low latency, thereby increasing user satisfaction. Employing advanced video encoding techniques, such as scalable video coding (SVC), improves the utilization of wireless resources and the network infrastructure. However, the cache trading policies for SVC videos in multi-provider networks have not been studied yet. In this article, we design a commercial trading system in which multiple CPs, each owning SVC videos, compete over renting the cache in multiple SBSs provided by an MNO. We model cache trading between the MNO and CPs as a social welfare maximization problem, whose objective is to maximize the trading profit while achieving the economic properties of rationality, balanced budget, and truthfulness. Since optimal allocation of random-size caches to multiple CPs is NP-hard, we devise an iterative trading mechanism based on double auction called DOCAT, wherein the cache of SBSs is segmented and traded in multiple rounds. In each round of the auction, the MNO and CPs price the cache segments based on their profit, then submit their asking and buying bids, respectively. Next, a many-to-one matching algorithm is run to efficiently find perfect matches between the cache segments and winning CPs. Numerical results based on a real video dataset show that DOCAT increases the social welfare of the system while satisfying the desired economic properties.Peer reviewe

Delay Analysis of Layered Video Caching in Crowdsourced Heterogeneous Wireless Networks

Caching popular content at small-cell base stations (SCBSs) and user equipments (UEs) can significantly reduce the network backhaul traffic while improving user satisfaction. This is also enabled by novel video encoding techniques, such as scalable video coding (SVC), which combine layers to offer content with different qualities without re-encoding. Despite some recent works, the performance of layered video delivery in crowd-sourced heterogeneous networks (HetNets) is still unexplored. This article provides an analytical characterization the delay of video delivery in a network with multiple cache-enabled SCBSs and UEs, each storing part of the available video layers based on their popularity. Accordingly, video requests from an UE can be served by either SCBSs or UEs nearby. Our main objective is to maximize the cache hit probability by caching appropriate video layers, thereby minimizing the average video delivery delay. We formulate the problem of minimizing the delivery delay of layered video caching asan integer linear program. We then apply the difference of convex functions technique to identify the set of optimal video layers to be cached at each SCBS and UE in an iterative manner. Our results obtained by using a real video dataset demonstrate that our proposed solution significantly reduces the video download time of all UEs in the network.Peer reviewe

Efficient and Fair Multi-Resource Allocation in Dynamic Fog Radio Access Network Slicing

Future wireless networks should meet heterogeneous service requirements of diverse applications, including interactive multimedia, augmented reality, and autonomous driving. The fog radio access network (Fog-RAN) is a novel architecture that enables efficient and flexible allocation of network resources to end users. However, guaranteeing application-specific service requirements while maximizing resource utilization is an open challenge in Fog-RANs. This article proposes a multi-resource Fog-RAN slicing scheme that maximizes network resource utilization and satisfies important economic properties: Pareto optimality, envy-freeness, and sharing incentive. The proposed solution considers both heterogeneous resources (i.e., bandwidth, storage and computing) and the different service levels defined in 5G networks. Accordingly, a two-level resource scheduling mechanism is devised to jointly allocate Fog-RAN resources to slices in two stages: a broker allocates resources to slices at fog nodes over a given time window; a slice hypervisor then allocates slice-specific resources at each fog node to users with a much shorter time scale. An extensive evaluation based on real-world datasets demonstrates that the proposed solution significantly increases the monetary gain of service providers, namely, by 32% to 60% compared to the state of the art, including dynamic hierarchical resource allocation and dynamic slicing with proportional allocation.Peer reviewe

PIS: A Multi-Dimensional Routing Protocol for Socially-Aware Networking

Socially-aware networking is an emerging paradigm for intermittently connected networks consisting of mobile users with social relationships and characteristics. In this setting, humans are the main carriers of mobile devices. Hence, their connections, social features, and behaviors can be exploited to improve the performance of data forwarding protocols. In this paper, we first explore the impact of three social features, namely physical proximity, user interests, and social relationship on users\u27 daily routines. Then, we propose a multi-dimensional routing protocol called Proximity-Interest-Social (PIS) protocol in which the three different social dimensions are integrated into a unified distance function in order to select optimal intermediate data carriers. PIS protocol utilizes a time slot management mechanism to discover users\u27 movement similarities in different time periods during a day. We compare the performance of PIS to Epidemic, PROPHET, and SimBet routing protocols using SIGCOMM09 and INFOCOM06 data sets. The experiment results show that PIS outperforms other benchmark routing protocols with the highest data delivery ratio with a low communication overhead

Mobile Crowdsourcing in Smart Cities

Local administrations and governments aim at leveraging wireless communications and Internet of Things (IoT) technologies to manage the city infrastructures and enhance the public services in an efficient and sustainable manner. Furthermore, they strive to adopt smart and cost-effective mobile applications to deal with major urbanization problems, such as natural disasters, pollution, and traffic congestion. Mobile crowdsourcing (MCS) is known as a key emerging paradigm for enabling smart cities, which integrates the wisdom of dynamic crowds with mobile devices to provide decentralized ubiquitous services and applications. Using MCS solutions, residents (i.e., mobile carriers) play the role of active workers who generate a wealth of crowdsourced data to significantly promote the development of smart cities. In this paper, we present an overview of state-of-the-art technologies and applications of MCS in smart cities. First, we provide an overview of MCS in smart cities and highlight its major characteristics.Second, we introduce the general architecture of MCS and its enabling technologies. Third, we study novel applications of MCS in smart cities. Finally, we discuss several open problems and future research challenges in the context of MCS in smart cities.Peer reviewe