Video Caching Research Articles

MNCATM: A Multi-Layer Non-Uniform Coding-Based Adaptive Transmission Method for 360° Video

With the rapid development of multimedia services and smart devices, 360-degree video has enhanced the user viewing experience, ushering in a new era of immersive human–computer interaction. These technologies are increasingly integrating everyday life, including gaming, education, and healthcare. However, the uneven spatiotemporal distribution of wireless resources presents significant challenges for the transmission of ultra-high-definition 360-degree video streaming. To address this issue, this paper proposes a multi-layer non-uniform coding-based adaptive transmission method for 360° video (MNCATM). This method optimizes video caching and transmission by dividing non-uniform tiles and leveraging users’ dynamic field of view (FoV) information and the multi-bitrate characteristics of video content. First, the video transmission process is formalized and modeled, and an adaptive transmission optimization framework for a non-uniform video is proposed. Based on this, the optimization problem required by the paper is summarized, and an algorithm is proposed to solve the problem. Simulation experiments demonstrate that the proposed method, MNCATM, outperforms existing transmission schemes in terms of bandwidth utilization and user quality of experience (QoE). MNCATM can effectively utilize network bandwidth, reduce latency, improve transmission efficiency, and maximize user experience quality.

Learning-based joint recommendation, caching, and transmission optimization for cooperative edge video caching in Internet of Vehicles

In an era dominated by multimedia information, achieving efficient video transmission in the Internet of Vehicles (IoV) is crucial because of the inherent bandwidth constraints and network volatility within vehicular environments. In this paper, we propose a cooperative edge video caching framework designed to enhance video delivery efficiency in IoV by integrating joint recommendation, caching, and transmission optimization. Leveraging deep reinforcement learning with the discrete soft actor–critic algorithm, our methodology dynamically adapts to fluctuating network conditions and diverse user preferences, aiming to optimize content delivery efficiency and quality of experience. The proposed approach combines recommendation and caching strategies with transmission optimization to provide a comprehensive solution for high-performance video services. Extensive simulation results demonstrate that our framework significantly outperforms traditional baseline methods, achieving superior outcomes in terms of service utility, delivery rate, and delay reduction. These results highlight the robust potential of our solution to facilitate seamless and high-quality video experiences in the complex and dynamic landscape of vehicular networks, advancing the capabilities of IoV content delivery.

Mobility-aware proactive video caching based on asynchronous federated learning in mobile edge computing systems

With the rapid advance of the Internet of Things (IoT) and the surging user-centric smart devices, video services such as short videos, real-time video streaming, and Virtual Reality (VR) gaming are emerging. Traditional cloud-centric caching is no longer able to satisfy the low-latency needs of mobile user groups. Predicting the most popular content in advance and caching it on edge devices such as small base stations (SBS) is a promising solution to alleviate network congestion and supply better Quality of Service (QoS) for mobile users. However, frequently changing popular content, limited edge caching resources, privacy of user data, and user mobility pose significant challenges to edge video caching. To tackle these challenges, we propose an efficient asynchronous federated learning (AFL)-based mobility-aware proactive video caching scheme (AF-MPVC), which significantly improves the overall network performance while preventing the risk of user data leakage. First, we model the proactive video caching problem in mobile edge computing systems and consider the impact of user mobility on AFL, representing the problem as an autoencoder (AE) model loss minimization problem. Then, a proactive video caching algorithm is proposed based on a filtering model, which uses the hidden features obtained from the trained AE model to compute the user similarity to find the popular videos that are most likely to be accessed by users and combine them with the most frequently requested videos. Meanwhile, we take into account the short duration of high mobility users staying on the current SBS and cache some videos to neighboring edge servers to have more redundant space to store more less popular videos. Finally, experiments on three real-world datasets demonstrate that the presented caching scheme outperforms other baseline caching schemes with respect to cache hit rate and latency reduction.

Adaptive Bitrate Video Caching in UAV-Assisted MEC Networks Based on Distributionally Robust Optimization

Adaptive Bitrate Video Caching in UAV-Assisted MEC Networks Based on Distributionally Robust Optimization

Edge caching and computing of video chunks in multi-tier wireless networks

This paper proposes a video caching and transcoding strategy for delay-constrained content delivery in multi-tier wireless networks. Particularly for multimedia services whose content can be encoded into multiple quality versions and consists of multiple chunks, we present an approach of caching chunks of an identical file separately in different network layers with different qualities. Given this caching policy, a complete file can be retrieved by receiving its chunks from different network tiers; therefore, a video transcoding and delivery method is jointly designed with caching to maximize the expected content quality with a strict delay constraint. Motivated by that caching is proactively conducted in advance of many user requests, the joint optimization problem is decomposed into caching and transcoding subproblems. The optimal transcoding policy and its transcoded qualities at different network tiers are analytically found by linear programming for a given cache state. Using the closed-form transcoded qualities, the caching policy is optimized to maximize the expected content quality with respect to randomness of user locations and channel gains. Due to the difficulty of expectation, the caching policy of each network layer is independently obtained by water-filling first, and an alternating optimization method is used to find the joint optimal caching policy satisfying the cache size constraints. Simulation results show that the proposed scheme succeeds in delivering content of 5% to 10% higher expected quality to users than comparison schemes which do not use partitioning for caching. Simultaneously, our approach meets the delay requirement and effectively manages the trade-offs arising from the varying storage and computational capacities within hierarchies, as well as video diversity.

Efficient Caching and Spreading of Traffic Videos by Optimized Topology in IoV

Efficient Caching and Spreading of Traffic Videos by Optimized Topology in IoV

Transcoding-Enabled Cloud–Edge–Terminal Collaborative Video Caching in Heterogeneous IoT Networks: An Online Learning Approach With Time-Varying Information

Transcoding-Enabled Cloud–Edge–Terminal Collaborative Video Caching in Heterogeneous IoT Networks: An Online Learning Approach With Time-Varying Information

CLoSER: Video caching in small-cell edge networks with local content sharing

We consider the problem of video caching in an edge network consisting of a set of small-cell base stations (SBS) that can share content among themselves over a high-capacity short-delay local network, or fetch the videos from a remote server over a long-delay connection. Even though the problem of minimizing the overall video playout delay in our framework is NP-hard, we develop CLoSER, an algorithm that can efficiently compute a solution that is close to the optimal, where the degree of sub-optimality depends on the worst case video-to-cache size ratio. In comparison with related prior work on video caching and streaming, CLoSER specifically focuses on the benefits of local sharing of the initial portion of the video content in reducing the video playout delay, and provides strong optimality guarantees with a low-complexity algorithm. We extend CLoSER to an online setting where the video popularities are not known a priori but are estimated over time through a limited amount of periodic information sharing between SBSs. With such online video popularity estimation, a distributed implementation of CLoSER requires zero explicit coordination between the SBSs and runs in O(NK+KlogK) time, where N is the number of SBSs (caches) and K the maximum number of videos. We carry out simulations using YouTube and Netflix video request traces, as well as synthesized traces with the same marginal distributions as the traces but varying degree of temporal correlations, and demonstrate that our algorithm uses the SBS caches effectively to reduce the video delivery delay and conserve the remote server’s bandwidth. We also show that it outperforms two other reference caching methods adapted to our system setting, over a wide range of remote-to-local bandwidth ratios. Further, we show how CLoSER extends to the scenario where each video may need to be cached in multiple bit-rates, as in Available Bit Rate (ABR) streaming.

Performance Analysis and Optimization for Layer-Based Scalable Video Caching in 6G Networks

Scalable video caching is a promising technique to alleviate backbone traffic in sixth generation (6G) networks, and to serve users with video quality that adapts to varying channel conditions. In this paper, we develop a layer-based scalable video caching technique with non-orthogonal transmission by taking advantage of the layer feature in the scalable video. In addition, the impact of different serving base station selection algorithms is investigated. Our results indicate that both the caching placement design and transmission scheme design dominate the caching performance. To evaluate the interplay of these two policies, a tractable metric of Caching Aided Data Rate (CADR) is characterized and maximized by jointly optimizing the aforementioned two policies. Together with extensive Monte Carlo simulations, numerical results are also evaluated in this paper, demonstrating that the proposed Layer-based video Caching scheme with Non-Orthogonal Transmission (LCNOT) can achieve higher CADR performance than other baseline schemes.

Fine-grained Caching and Resource Scheduling for Adaptive Bitrate Videos in Edge Networks

With the easy access to mobile networks and the proliferation of video applications, video traffic is occupying a great portion of the network traffic, which poses a new challenge of how to alleviate the heavy backhaul traffic and ensure the high quality of experience for video services. As a promising solution towards addressing this challenge, video caching in edge networks has recently received significant attention, which mostly considers the video popularity and the user preference for the video. However, few studies consider the user behavior and the user preference for different parts of the video that indeed have an essential impact on caching efficiency. Hence, this article proposes a new caching and resource scheduling scheme for adaptive bitrate videos by incorporating these fine-grained factors. We first model the video service problem as a nonlinear integer programming problem, which can be divided into a cache placement problem and an online resource scheduling problem. Then, we design efficient algorithms based on several techniques, including greedy strategy, relaxation, and rounding, to solve the two problems. Extensive experimental results based on two real-world datasets show that the proposed solution achieves superior performance compared with several state-of-the-art caching approaches.

To transcode or not? A machine learning based edge video caching and transcoding strategy

The variable network conditions of end-users demand different resolutions, formats, and bitrate versions of videos to be delivered over the network. Fetching each video from the Content Delivery Network (CDN) burdens all network layers. A promising solution is to leverage Mobile Edge Computing (MEC). This paper presents a Machine Learning based caching and transcoding model, which helps release the burden on the backhaul links of the network. The purposed scheme contains a task scheduler and time estimator. The time estimator predicts the job’s transcoding time based on the Virtual Machines (VMs) load. The task scheduler maps the transcoding task to different VMs regarding the cost feasibility, Quality of Service (QoS) of the users, and the cost-to-performance ratio of VMs. For this purpose, we prepare a dataset of 500 videos and transcode each video in every lower representation using Amazon Elastic Compute Cloud (EC2). The time estimator is trained on 77% of the video dataset containing more than 80,000 transcoding time data of different videos. The simulation results show that the proposed scheme outperforms its counterparts in terms of cost, average delay perceived by the user, and backhaul burden.

A Novel Video-Spreading Strategy Based on the Joint Estimation of Social Influence and Sharing Capacity in Wireless Networks

In this paper, we propose a novel video-sharing strategy based on the joint estimation of social influence and sharing capacity in wireless networks (SSISC), which promotes the scale and efficiency of video spread and ensures the balance of supply and demand. SSISC designs an estimation model of video-sharing gains by investigating social influence levels, sharing capacities (including capacities of information dispatching and video delivery), and predicted expansion scale. Some social parameters (e.g., centrality of degree and betweenness, and average shortest distance) and some parameters of sharing performance (e.g., number of forwarded messages and cached videos, the average time of transmission and freeze) are used to evaluate social influence, capacities of information dispatching, and video delivery; video interest levels, social relationship levels, and historical push success rates are used to predict video proliferation scale. A video-spreading strategy based on the assistance of spread nodes is designed, which controls the process of video push according to available bandwidth and push priority to balance supply and demand and ensure user experience quality. Extensive tests show how SSISC achieves much better performance results in comparison with other state-of-the-art solutions.

An optimal strategy for UAV-assisted video caching and transcoding

An optimal strategy for UAV-assisted video caching and transcoding

A GNN-based proactive caching strategy in NDN networks

As people spend more time watching and sharing videos online, it is critical to provide users with a satisfactory quality of experience (QoE). Leveraging the in-network caching and named-based routing features in Named Data Networks (NDNs), our paper aims to improve user experience through caching. We propose a graph neural network-gain maximization (GNN-GM) cache placement algorithm. First, we use a GNN model to predict users’ ratings of unviewed videos. Second, we consider the total predicted rating of a video as the gain of the cached video. Third, we propose a cache placement algorithm to maximize the caching gain and actively cache videos. Cache replacement is implemented based on the cache gain ranking of videos, with higher cache gain videos replacing lower cache gain videos. We compare GNN-GM with two state-of-the-art caching strategies, namely the NMF-based caching strategy and GNN-CPP. GNN-GM is also compared with two traditional caching strategies, LCE and LRU, LCE and FIFO. We evaluate the five caching strategies using real-world datasets in a tree network topology, a real-world network topology GEANT, and various random topologies. The experimental results show that our caching policy significantly improves cache hit ratio, latency and server load. Notably, GNN-GM achieves a 25% higher cache hit rate, 5% lower latency and 7% lower server load than GNN-CPP in GEANT.

Towards Real-Time Video Caching at Edge Servers: A Cost-Aware Deep Q-Learning Solution

Given the rapid growth of user-generated videos, internet traffic has been heavily dominated by online video streaming. Caching videos on edge servers in close proximity to users has been an effective approach to reduce the backbone traffic and the request response time, as well as to improve the video quality on the user side. Video popularity, however, can be highly dynamic over time. The cost of cache replacement at edge servers, particularly that related to service interruption during replacement, is not yet well understood. This paper presents a novel lightweight video caching algorithm for edge servers, seeking to optimize the hit rate with real-time decisions and minimized cost. Inspired by recent advances in deep Q-learning, our DQN-based online video caching (DQN-OVC) makes effective use of the rich and readily available information from users and networks. We decompose the Q-value function as a product of the video value function and the action function, which significantly reduces the state space. We instantiate the action function for cost-aware caching decisions with low complexity so that the cached videos can be updated continuously and instantly with dynamic video popularity. We used video traces from Tencent, one of the largest online video providers in China, to evaluate the performance of our DQN-OVC and to compare it with state-of-the-art solutions. The results demonstrate that DQN-OVC significantly outperforms the baseline algorithms in the edge caching context.

Caching and 3D Deployment Strategy for Scalable Videos in Cache-Enabled Multi-UAV Networks

Unleashing the potential of unmanned aerial vehicles (UAVs) to play the role of caching devices for furnishing layered video services to ground users (GUs) holds tremendous promise. However, it is crucial to strike a balance between the quality and the diversity of video content when deciding the placement strategies of video caching. In this study, we examine the feasibility of implementing a layered caching scheme utilizing scalable video coding (SVC) in three-dimensional (3D) multi-UAV networks with cache capabilities. The objective is to investigate the minimization of the cumulative video access delay experienced by all GUs through the joint design of layered scalable video caching, user-UAV association, and 3D UAV deployment. This approach enables the network to offer video services across a range of quality levels. We have formulated an intricate mixed-integer non-convex optimization problem which poses a significant challenge to solve directly. Then we decompose the formulated problem into three interrelated sub-problems and put forth specific algorithms designed to address each component, i.e., layered caching strategy and UAV's two-dimensional (2D) deployment optimization with penalty based difference-of-convex (penalty-DC) framework, user-UAV association optimization with bipartite graph based linear programming (LP) relaxation, as well as UAV altitude optimization with successive convex approximation (SCA) method. Then we introduce a highly efficient iterative algorithm that employs an alternate optimization approach for three sub-problems until convergence. Our simulation results demonstrate that our proposed algorithm surpasses other benchmark schemes significantly in terms of mitigating video access delay.

Dynamic Clustering for Low-Delay Delivery of Video Content Cached in MEC Servers

For the purpose of video caching and low-delay video delivery to the end-users, multiaccess edge computing (MEC) servers are commonly grouped into clusters to efficiently exploit the limited storage resources of the MEC servers. In this article, we first introduce a methodology for analysis of the video delivery delay using the queuing theory. Our analysis shows that the video delivery delay is mainly affected by the arrival rate of the video requests. Furthermore, we show a tradeoff between the ratio of videos found in the MEC servers within the same cluster and the transmission delay of video contents. To reduce the video delivery delay, we propose a dynamic MEC server clustering (DyMECC) algorithm that determines the cluster size at each time interval by solving analytically derived equations considering the actual arrival rate of video requests. It also acts as a congestion avoidance mechanism for the communication interfaces among the MEC servers. Via simulations, we show that the DyMECC reduces the video delivery delay about 15% in light load conditions and by more than five times in heavy load conditions compared to state-of-the-art works while also reduces the load of the communication interfaces among the MEC servers by more than 45%.

Online Orchestration of Collaborative Caching for Multi-Bitrate Videos in Edge Computing

In the traditional video streaming service provisioning paradigm, users typically request video contents through nearby Content Delivery Network (CDN) server(s). However, because of the uncertain wide area networks delays, the (remote) users usually suffer from long video streaming delay, which affects the quality of experience. Multi-Access Edge Computing (MEC) offers caching infrastructures in closer proximity to end users than conventional Content Delivery Networks (CDNs). Yet, for video caching, MEC's potential has not been fully unleashed as it overlooks the opportunities of collaborative caching and multi-bitrate video transcoding. In this paper, we model and formulate an Integer Linear Program (ILP) to capture the long-term cost minimization problem for caching videos at MEC, allowing joint exploitation of MEC with CDN and real-time video transcoding to satisfy arbitrary user demands. While this problem is intractable and couples the caching decisions for adjacent time slots, we design a polynomial-time online orchestration framework which first relaxes and carefully decomposes the problem into a series of subproblems solvable in each individual time slot and then converts the fractional solutions into integers without violating constraints. We have formally proved a parameterized-constant competitive ratio as the performance guarantee for our approach, and also conducted extensive evaluations to confirm its superior practical performance. Simulation results demonstrate that our proposed algorithm outperforms the state-of-the-art algorithms, with 13.6% improvement on average in terms of total cost.

VC-PPQ: Privacy-Preserving Q-Learning Based Video Caching Optimization in Mobile Edge Networks

Mobile edge caching has great advantages in alleviating video traffic pressure and reducing transmission delay, which is considered as a hopeful solution to improve video resource utilization and achieve faster service response. However, due to insufficient and non-real time acquisition of the status between edge servers and mobile users, it is still difficult for existing solutions to improve the video quality of experience (VQoE) for mobile users. Besides, due to the mobility of users and malicious third-party attacks, users may suffer the risk of personal privacy (e.g. location, trajectory, preference, etc.) leakage when requesting video services that are scattered in different areas. In order to efficiently deal with these problems, this paper proposes a privacy-preserving Q-learning based video caching optimization framework in mobile edge networks (VC-PPQ), including a privacy scheme for user data and a dynamic video caching algorithm. Specifically, in the first task, the local differential privacy model is leveraged to protect the locations and preferences of users. Then, a data aggregation model is provided to make trade-off between aggregation accuracy and privacy protection. In the second task, we further obtain the video popularity in user's area by a computation service model, then formulate a caching objective function combined with transcoding technologies. Afterwards, Q-learning is adopted to obtain the goal of caching optimization, which can achieve low delay and high hit ratio. Finally, extensive simulations prove that our VC-PPQ framework delivers prominent performance advantages in terms of user privacy and caching services, which greatly improves the VQoE of users.

A Video Cache Replacement Scheme based on Local Video Popularity and Video Size for MEC Servers

A Video Cache Replacement Scheme based on Local Video Popularity and Video Size for MEC Servers