Video Caching Research Articles

Leveraging recommendation systems for improving caching emerging short video in content delivery network

AbstractShort video applications have rapidly become one of the most popular online entertainments. It is also one of the most important ways of social networking. However, the request pattern of short videos shows the characteristics of highly dynamic compared to traditional video. First, the access of short video is changing quickly, that is, user shows less preferences. Second, most short videos have no tag descriptions. These result in a significant reduction in caching efficiency of content delivery network (CDN) server, which is used to cache short videos. To address these challenges, this article proposes a recommendation system to optimize short video caching. For lack of tag description, this article proposes event detection method to recognize tags based on improved dense trajectory and concept dictionary video feature extraction. Then the collaborative filtering method is used to generate caching recommendation list for CDN to guide short video caching. Simulation results show that the proposed method can significantly improve the efficiency of short video caching for CDN.

Self-adaptive power control with deep reinforcement learning for millimeter-wave Internet-of-vehicles video caching

Video delivery and caching over the millimeter-wave (mmWave) spectrum is a promising technology for high data rate and efficient frequency utilization in many applications, including distributed vehicular networks. However, due to the short handoff duration, calibrating both optimal power allocation of each base station toward its associated vehicles and cache allocation are challenging for their computational complexity. Heretofore, most video delivery applications were based on on-line or off-line algorithms, and they were limited to compute and optimize high dimensional objectives within low-delay in large scale vehicular networks. On the other hand, deep reinforcement learning is shown for learning such scale of a problem with an optimized policy learning phase. In this paper, we propose deep deterministic policy gradient-based power control of mmWave base station (mBS) and proactive cache allocation toward mBSs in distributed mmWave Internet-of-vehicle (IoV) networks. Simulation results validate the performance of the proposed caching scheme in terms of quality of the provisioned video and playback stall in various scales of IoV networks.

RAN-aware adaptive video caching in multi-access edge computing networks

Videos are expected to be a primary contributor to an anticipated massive surge in mobile network data. Caching the videos within the mobile network can significantly reduce the network load and Operational Expenditure (OPEX) for mobile network operators. Multi-access Edge Computing (MEC) can enable the video caching by providing processing and storage capabilities within the network. However, content providers usually employ Dynamic Adaptive Streaming over HTTP (DASH) for video streaming, which contains multiple bit-rate representations of videos. Constrained by its capacity, MEC can not cache all representations of popular videos. Video transcoding mitigates this issue to a certain extent by converting the higher available video bit-rate to a requested lower one; but, it can quickly exhaust the available edge processing power by transcoding a large number of videos in parallel. Therefore, caching appropriate video bit-rates that can serve the maximum number of users in the network is a non-trivial problem. To resolve this problem and to efficiently utilize the resources (processing and storage) at the network edge, we took a non-traditional approach for video caching that utilizes the network information provided by MEC's Radio Network Information (RNI) Application Program Interface (API). In particular, RNI API provides Radio Access Network (RAN) status information that can be employed to estimate the probability distribution of requested video qualities. In this work, we formulate the video caching problem as an Integer Linear Programming (ILP) for the hit-rate maximization. Since the optimization problem requires the knowledge of all future requests, it obviously cannot be used in real-time. Therefore, we develop a RAN-aware Adaptive VidEo cachiNg (RAVEN) method that uses network information to make an informed decision for video bit-rate selection in video caching coupled with transcoding and maximizes the number of served users form the network edge. Simulation results demonstrate that the RAVEN significantly outperforms state-of-the-art algorithms in the domain and performs closer to the optimal solution.

Multi-Tier Caching and Resource Sharing for Video Streaming in 5G Ultra-Dense Networks

In this letter, we formulate a multi-tier caching and resource sharing (CRS) optimization problem for a high video streaming performance in 5G ultra-dense networks. Particularly, in user tier, the optimal sets of spectrum owning users (SUs) with available downlink resources, caching users (CUs) with cached video versions, and normal users (NUs) requesting the video versions are tripartite for video streaming over device-to-device (D2D) communications. In femto tier, the optimal caching strategy is found to indicate which video versions and where to cache them in the femtocell base station (FBSs). By cooperating with the video versions cached in macro tier, i.e., macro base station (MBS), the mobile users (MUs) can receive the video versions from D2D communications, FBSs, and MBS flexibly. The objective of the CRS solution is to maximize the video playback quality, while saving the caching storage of FBSs and satisfying a given throughput required by MUs and a given target signal to interference plus noise ratio of SUs.

A smart transmission optimization mechanism for sports events based on edge computing

With the rise of smart cities in today's society, more and more people pay attention to sports events with high requirements on the transmission quality. However, due to the fact that the audiences of sports video are relatively concentrated, it presents a stronger locality of time and space. Hence, the traditional video transmission schemes are no longer suitable for the transmission of sports video. In this way, this paper studies the smart sports video transmission optimization scheme based on edge computing. First of all, this paper designs a cloud incremental scalable sports video view popularity prediction model, based on which an on‐demand and customized video coding mechanism is introduced to maximize the efficiency of computing resource allocation. Secondly, this paper designs a smart sports video caching mechanism based on edge computing to eliminate a large number of video transmission redundancy. Finally, a large‐scale simulation experiment is built based on the real sports video data, and the experimental results fully show that the proposed scheme can improve the user experience on video quality and relative smoothness.

Scalable Video Transmission in Cache-Aided Device-to-Device Networks

Scalable video coding (SVC) and video caching are two promising techniques in the 5th generation networks to improve the users' quality of experience (QoE) in terms of video retrieval. In this paper, we study the video content retrieval in cache-aided device-to-device (D2D) networks, where each video content is coded into multiple layers via SVC. In video caching placement phase, the probabilistic caching placement policy is applied, while in content retrieval phase, the non-orthogonal transmission scheme is utilized. Besides, different D2D transmitter selection algorithms are considered and cache-aided data rate (CADR) is formulated as a metric in this paper, and it is maximized by jointly optimizing the probability caching policy and the power allocation policy in two phases. Analytical results show that probabilistic caching policy incorporated with power domain non-orthogonal transmission scheme can achieve significant benefits compared with other benchmark schemes.

Popularity-Based and Version-Aware Caching Scheme at Edge Servers for Multi-Version VoD Systems

Recently, many video-on-demand (VoD) providers have begun storing multiple versions of the same video to offer multiple-quality video services with different bitrates to users, called multi-version VoD. To improve users’ quality of experience (QoE), it is a good idea to cache videos at edge servers in multi-version VoD systems. However, determining which versions of which videos should be cached or replaced in an edge server is still a major challenge for a multi-version VoD system because of its limited cache storage. In this paper, we propose a popularity-based and version-aware caching scheme (PVCS) at edge servers for multi-version VoD systems. First, based on video popularity, we formulate cache placement as a knapsack problem under constraints such as the cache storage and transcoding computation of the edge server, which aims to maximize the cache hit ratio. Second, we use the transcoding relations among versions to calculate a version-aware caching profit when caching a certain version or multiple versions of a video. The version-aware caching profit is the basis for the subsequent cache replacement algorithm. Third, we propose two algorithms, the video cache placement (VCP) algorithm and the video cache replacement (VCrP) algorithm, to solve the cache placement and replacement problems respectively. VCP utilizes the Lagrangian relaxation algorithm to decide which video files should be cached initially, and VCrP decides which video files cached at the edge server will be replaced dynamically based on the version-aware profit. In this way, the PVCS can improve the cache hit ratio and decrease the average start-up delay. Our simulation results have shown that the PVCS outperforms the other schemes in terms of the cache hit ratio and the average start-up delay.

DASH based video caching in MEC-assisted heterogeneous networks

Recently, the demand for video applications has become more and more pressing. To save backhaul bandwidth and reduce the network delay, Mobile Edge Computing (MEC) has arisen as a promising technique in future mobile networks. It can provide the storage, computing and networking resources in close-proximity to the mobile users through deploying multiple MEC servers at mobile edge. However, how to effectively utilize the limited storage capacity of these MEC servers is still the major challenge, especially when Dynamic Adaptive Streaming over HTTP (DASH) is used for video transmission. Recent studies have shown that layered encoding is very suitable for streaming service using DASH standard. Therefore, in our paper, combining with the feature of DASH-based scalable video transmission, we first design a novel multi-server cooperative scheme with software-defined networking (SDN) architecture for future MEC-assisted heterogeneous networks, to effectively manage and schedule the available resources, thus guaranteeing the requested video delivered to the clients with higher quality and lower delay. And then, based on the above scheme, the end-end delay is carefully analyzed for each real video streaming. With a full consideration of the averag end-to-end delay of video streaming and the provided video quality of MEC servers, we finally propose a Dynamic Programming based Adaptive Caching Algorithm (DPACA) to obtain an optimized caching strategy under the constraint of cache storage capacity at each MEC sever. Simulation results show that, our algorithm can improve the cache hit ratio while significantly reducing the end-to-end delay. Moreover, our algorithm has higher flexibility and adaptability.

Device-to-device streaming video caching for cellular download cost reduction

Abstract This paper proposes a Device-to-Device cooperative caching framework for streaming video consumption by mobile users. For downloading video with cooperative caching, a mobile device first searches within its neighboring Bluetooth-connected peers for whole or parts of the requested video before downloading them from a Content Provider’s server. A specific cooperative caching framework, namely, value-based caching is proposed in which the value of caching a hierarchically coded streaming video segment is defined for given pricing and video sharing models. Within this framework, we develop an Adaptive Quality (AQ) provisioning algorithm that minimizes the overall video content provisioning cost incurred due to the bandwidth usage of a cellular network. Detailed simulation experiments for functional validation and performance evaluation in comparison with other comparable caching strategies are then performed under different protocols and network parameters. It is shown that the proposed mechanism is able to appreciably reduce the overall video provisioning cost in the presence of end-user mobility.

Smart collaborative video caching for energy efficiency in cognitive Content Centric Networks

Due to the prosperity of online media, caching video content has become an important measure to improve the user experience and alleviate the link burden in wireless caching networks. However, facing to perceptual requirements of video users, Scalable Video Coding (SVC) technique has been cumulatively utilized in wireless caching networks, which can guarantee acceptable video quality. In this paper, we propose a smart collaborative video caching policy to boost energy efficiency (EE) in Cognitive Content Centric networking (C-CCN), where the spectrum sharing characteristic of cognitive ratio (CR) is integrated into CCN. With consideration of the imperfect channel-state information (CSI), the collision interference exists between the primary users (PUs) and secondary base stations (SBs). Inspired by this, we derive the expressions of successful content delivery rate (SCDR) and total power consumption for the proposed policy. By using these expressions, the optimal EE model under the caching size constraint is formulated as an optimal content placement problem, which can be solved by the standard gradient projection method. Numerical and simulation results show the efficacy and superiority of the proposed policy compared with some traditional caching schemes.

Multi-access edge computing in cellular networks

Multi-access edge computing (MEC) is an extension of cloud computing technology to provide an application deployment platform close to the end users. This platform helps to deploy real-time, compute and resource-intensive applications like video caching, augmented reality, virtual reality, and Internet of Things at the edge. MEC enhances the performance of 4G and 5G networks since it achieves reduced application latency and optimizes the backhaul bandwidth utilization. Due to these benefits, MEC has become one of the key enablers of 5G technology. This paper covers evolution, architecture, current standardization activities, and use cases of MEC in cellular networks. The paper also emphasizes on how key enablers of 5G like software defined networking, network function virtualization, quality of service handling, and service based interface model will handle faster connectivity, huge data demand with less backhaul congestion, large number of devices and real-time applications as compared to MEC in 4G. Further, MEC deployment approaches in 4G and 5G are discussed, which play a vital role in achieving the requirement of 5G services.

Cooperative Caching for Multiple Bitrate Videos in Small Cell Edges

Caching popular videos at mobile edge servers (MESs) has been confirmed as a promising method to improve mobile users (MUs) perceived quality of experience (QoE) and to alleviate the server load. However, with the multiple bitrate encoding techniques prevalently employed in modern streaming services, caching deployment is challenging for the following three facts: (1) cooperative caching should be explored for MUs located at overlapped coverage areas of MESs; (2) there exists tradeoff consideration for caching either high bitrate videos or high diversity videos; and (3) the relationship between MU perceived QoE and MU received bitrate, known as QoE function, varies in different services. Aiming to maximize the MU perceived QoE, we formulate the multiple bitrate video caching problem, and prove this problem is NP-hard for any given positive and strictly increasing QoE function. We then propose a polynomial complexity algorithm based on a general QoE function, which can achieve an approximate ratio arbitrarily close to 1/2. Specifically, for a linear QoE function, we explore useful property of optimal solutions, based on which more efficient algorithms are proposed. We demonstrate the effectiveness of our solutions via both theoretical analysis and extensive simulations.

Joint Video Caching and Processing for Multi-Bitrate Videos in Ultra-Dense HetNets

Caching popular videos at the edge has been confirmed as a promising way to support low-latency video transmission and alleviate the backhaul traffic burden. Meanwhile, mobile edge computing (MEC) has also been regarded as an effective solution to meet the 5G low-latency service requirements. In this article, we propose to fully utilize both the storage and computing resources at edge servers to support multiple bitrate video streaming. We design the video caching, processing, and user association models that aim to minimize the average retrieval latency of all users. This problem is modeled as a mixed-integer bilinear problem, which is NP-hard . We show that under practical constraints on storage, bandwidth, and processing capacity, the problem does not exhibit sub-modular property and the performance of a greedy algorithm may not be strictly guaranteed. To deal with this challenging problem, we decompose the original problem into a cache placement problem and a user-BS association problem, while still preserving the interplay between the two sub-problems. A linearization and rounding algorithm, including: (i) a greedy rounding proactively caching scheme and (ii) a random-rounding user-BS association scheme, is then proposed, with performance bounds derived. Extensive simulation results show that the proposed scheme can achieve a near-optimal performance under various storage, computing capacity, and downlink bandwidth settings.

Dynamic Resource Allocation for Scalable Video Streaming in OFDMA Wireless Networks

Mobile video streaming is a successful example of Cyber-Physical-Social Systems (CPSS). How to schedule network resources and provide better mobile video streaming services for mobile users are very important. Scalable video streaming is regarded as a promising technology in wireless networks where the cognitive femtocells are overlaid within the coverage area of a macrocell network. In this paper, we study dynamic resource allocation for scalable video streaming over cache-enabled wireless networks with time-varying channel conditions. We formulate the scalable video streaming problem as a stochastic optimization problem which aims at maximizing the time-averaged system utility subject to the time-averaged video cache constraint at the server and the cross-tier interference constraint on the primary user under the sparse deployment scenario of femtocells. By employing the Lyapunov optimization theory, we design a dynamic cache and resource allocation (DCRA) algorithm to solve this problem. Furthermore, the problem is decomposed into three subproblems, i.e., video layer selection, cache placement, and wireless resource allocation. Via solving these subproblems, we derive the video layer selection and cache placement strategies, and a wireless resource allocation algorithm to manage the cross-tier interference. Simulation results demonstrate the advantages of the proposed DCRA for streaming scalable video over time-varying wireless networks.

Incentivizing Mobile Video Users With Data Sponsoring and Edge Caching

Recently, edge caching has emerged as a new data sponsoring scheme, where the devices on the edge network, e.g., 4G/5G base stations, cache video contents and directly delivery them to mobile video users. Such caching schemes have significantly decreased the backhaul congestion, reduce the delivery latency, thus attract more mobile video users and monetize a huge number of videos. In this paper, we consider a hybrid caching schemes system, which combines edge cache sponsoring (ECS) and traditional cellular data sponsoring (CDS), where the content providers display advertisements to mitigate the cellular data downloading expense and provide attractive contents for mobile video users. In the system, we investigate the cooperative scenario and the competitive scenario between the two sponsoring schemes, and a mobile video user can select one scheme or neither of them for the requests of a video. In the first scenario, we focus on achieving the maximal total benefit of content providers by optimizing CDS and ECS schemes together. In the second scenario, we separately optimize ECS and CDS schemes and maximize the benefits of corresponding content providers. To illustrate the effectiveness of the two scenarios, we formulate the system as a two-stage game: 1) in the first stage, content providers (i.e., leaders) choose the sponsor effort in sponsor schemes (cooperatively or competitively); 2) in the second stage, mobile video users (i.e., followers) select their sponsor preferences. We conduct numerical results to explore the sub-game perfect equilibrium and find that the joint utilization of the two sponsor schemes can benefit the mobile video users, i.e., when ECS and CDS compete with each other, MUs can benefit 36% ~ 140% more than the case where there exists only one sponsor scheme. While when CPs cooperate with each other, their total payoff is maximized, the mobile video users' payoff also increases. Moreover, we find that ECS can benefit the content providers more than CDS when sponsor revenue is high, and this indicates that ECS is a promising sponsor scheme for the high-value contents.

Reconstructing cached video stream content:- Part 2

In 2018, Horsman (2018a; 2018b) provided guidance for the reconstruction of cached stream remnants following use of the Periscope, Facebook Live and YouTube platforms. These works confirmed that video stream content can be cached to a local device when viewed via an Internet browser, and that following the provided methodology, video content can be rebuilt for subsequent viewing. This work provides an analysis of a further six platforms; Twitch, Youtube Live, Mixer, Ustream.tv, Smashcast.tv, and Younow. An analysis of localised stream caching is provided along with methodologies to view this content, ensuring support to those investigating offences where streaming may provide pertinent evidence of a criminal act or in fact be illegal per se.

Transmission Protocol Customization for Network Slicing: A Case Study of Video Streaming

In this article, we propose a software-defined networking (SDN)-based transmission protocol (SDTP) for futuregeneration networks. Different services are supported by dedicated network slices, each of which can be operated separately with a customized transmission protocol. In particular, we focus on video streaming services supported by in-network caching functions. By exploiting the flexibility of scalable video coding (SVC) and SDN control intelligence, we present an in-network bottleneck queue management strategy in conjunction with a novel selective caching policy (SCP) for congestion detection and mitigation. Additionally, an enhanced transmission (ET) scheme is devised to improve the video user experience by opportunistically requesting cached video packets when network conditions permit. The proposed protocol can adapt to traffic dynamics and varying service requirements, and it is shown to effectively alleviate network congestion and provide a balanced user experience. Extensive simulation results are presented to validate the proposed protocol in terms of in-network queue stability and userperceived video quality.

QoE-Driven Transmission-Aware Cache Placement and Cooperative Beamforming Design in Cloud-RANs

Pre-caching popular videos at base stations (BSs) is a cost-effective way to significantly alleviate the backhaul pressure. With the video caching, the cache placement and the transmission strategy are intertwined with each other and jointly affect the system performance. Furthermore, the cache placement is updated in a much longer timescale than the transmission strategy. In this paper, the long-term transmission-aware cache placement and the short-term transmission strategy are designed to enhance the quality of experience (QoE) for the video streaming in cloud radio access networks (cloud-RANs). Specifically, consider a cache-enabled cloud-RAN, video contents are cached at BSs, and user requests are cooperatively satisfied by multiple BSs via the cooperative beamforming. To improve the weighted sum of users’ QoE, the long-term transmission-aware caching problem in the caching stage and the short-term transmission problem in the delivery stage are respectively formulated, taking into account the backhaul capacity constraint, the transmission power constraint, and the storage size constraint. For the caching problem, the sample average approach is first used to approximate the long-term average QoE value. Then, cache placement strategies are devised in both centralized and distributed manner. For the transmission problem, the full-cooperative beamforming scheme is studied with the optimized cache placement, and an iterative algorithm is proposed. Simulation results show that our proposed transmission-aware cache placement and transmission strategies can achieve higher QoE performance than other cache placement and transmission strategies.

Budgeted video replacement policy in mobile crowdsensing

Mobile crowdsensing offers a new platform that recruits a suitable set of users to collectively complete an information collection/sensing task through users’ equipped devices. As a special case, video crowdsensing is to collect different video segments of the same event that are taken separately by the built-in cameras of mobile devices, and then combine them into a complete video. Mobile crowdsensing has attracted considerable attention recently due to the rich information that can be provided by videos. However, because of the limited caching space, a suitable video replacement policy is necessary. In this paper, we propose a Budgeted Video replaCement policy in mobile Video crowdsensing (BVCV), which first determines a video segment’s value according to its caching situation and natural attributes. Then, we formulate the video caching problem as a budgeted maximum coverage problem, which is a well-known NP-hard problem. Finally, we propose a practical greedy solution and also infer the approximate ratio, which could be regarded as the lower bound of BVCV to the optimal solution. Our experiments with the real mobility datasets (StudentLife dataset, Buffalo/phonelab-wifi dataset) show that, the proposed budgeted video replacement policy achieves a longer successfully delivered video length, compared with other general replacement policies.

On the Interaction of Video Caching and Retrieving in Multi-Server Mobile-Edge Computing Systems

Mobile-edge computing (MEC) could support edge caching and processing for video service, which essentially changes “cache and transmit” mechanism and provides another video retrieval mode. However, existing caching policies are usually inefficient, mainly due to lack of: 1) interaction of video caching and retrieving which happen in different timescales and 2) collaboration among servers. As motivated, we jointly consider long-term caching placement and short-term video retrieval mode selection in coordinated multi-server MEC systems to enrich user experience. Besides, we propose a sample average approximation-based two-phase algorithm which outmatches existing approaches in terms of content access delay and hit ratio.