Video Requests Research Articles

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.

Streamlining streaming videos: moving towards an efficient streaming video workflow in Alma/Primo

This case study intends to demonstrate the lessons learned as an academic library went through several changes to accommodate an increase in streaming video requests on their campus. A historical outline of the changing landscape of streaming video procedures and platforms will be provided for context. The iterative changes in vendor relations, internal workflows, and resources allocated to adapt to the changing nature of streaming videos as well as lessons learned during implementation will be presented.

Future Proofing Streaming Video Acquisitions: A Medium Sized Academic Library Adapts

ABSTRACT The pedagogical appeal of using film in higher education, while not new, has undeniably grown since the COVID-19 pandemic necessitated remote teaching for health and safety reasons. This dramatic rise in streaming video requests is challenging for academic libraries of all sizes. Yet, smaller- and medium-sized institutions have personnel who already juggle multiple roles and responsibilities, resulting in chaos and the need for new workflows. This article will reflect on the past 24 months of managing streaming video at the University of San Diego’s Copley Library, comparing the remote instruction period to the hybrid teaching that followed.

Evaluation of streaming video usage in a university library before, during, and after the COVID-19 pandemic lockdown

As the COVID-19 pandemic lockdown and remote course delivery recede in the rear-view mirror, academic librarians look to what the future holds for streaming video. During 2020 and 2021, library-funded streaming video requests soared while library budgets declined. Librarians are concerned that this trend will continue beyond budgetary limits. This paper describes an analysis of streaming video usage from 2019 through 2022 at California State University, Fresno. The results provide evidence that the elevated growth rate is tapering off at this institution, but the medium remains popular. The impact of streaming media on library budgets and collection development policies is also discussed. Our evaluation is distinguished by the inclusion of data from before, during, and after the pandemic lockdown.

A Time Pattern-Based Intelligent Cache Optimization Policy on Korea Advanced Research Network

Data is growing quickly due to a significant increase in social media applications. Today, billions of people use an enormous amount of data to access the Internet. The backbone network experiences a substantial load as a result of an increase in users. Users in the same region or company frequently ask for similar material, especially on social media platforms. The subsequent request for the same content can be satisfied from the edge if stored in proximity to the user. Applications that require relatively low latency can use Content Delivery Network (CDN) technology to meet their requirements. An edge and the data center constitute the CDN architecture. To fulfill requests from the edge and minimize the impact on the network, the requested content can be buffered closer to the user device. Which content should be kept on the edge is the primary concern. The cache policy has been optimized using various conventional and unconventional methods, but they have yet to include the timestamp beside a video request. The 24-h content request pattern was obtained from publicly available datasets. The popularity of a video is influenced by the time of day, as shown by a time-based video profile. We present a cache optimization method based on a time-based pattern of requests. The problem is described as a cache hit ratio maximization problem emphasizing a relevance score and machine learning model accuracy. A model predicts the video to be cached in the next time stamp, and the relevance score identifies the video to be removed from the cache. Afterwards, we gather the logs and generate the content requests using an extracted video request pattern. These logs are pre-processed to create a dataset divided into three-time slots per day. A Long short-term memory (LSTM) model is trained on this dataset to forecast the video at the next time interval. The proposed optimized caching policy is evaluated on our CDN architecture deployed on the Korean Advanced Research Network (KOREN) infrastructure. Our findings demonstrate how adding time-based request patterns impacts the system by increasing the cache hit rate. To show the effectiveness of the proposed model, we compare the results with state-of-the-art techniques.

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.

PPVC: Online Learning Toward Optimized Video Content Caching

Today’s Internet traffic has been dominated by video contents. To efficiently serve online videos for millions of users, it is essential to cache frequently requested videos on various devices such as edge servers, personal computers, etc. Existing caching algorithms are mainly designed by leveraging the information of past request records. However, it is insufficient to only use simple statistics of past request records, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , video popularity which ignores the occurrence time of past video request events and the discrepancies among individual devices. In this paper, we propose a radically different learning-based video caching algorithm for Internet devices, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PPVC (Point Process based Video Cache)</i> which can take exact video request patterns into account. By utilizing Hawkes Process (HP), we are able to link the future video request rates of a device with three kinds of historical records, namely, historical requests for the same video launched by the device itself, historical requests for the same video from other devices with a similar request pattern, and historical requests for other similar videos from the same device. The video request patterns can be efficiently computed via Singular Value Decomposition (SVD). Parameters linking these historical events can be determined by maximizing the likelihood of historical events. To further reduce the computation load, we also propose an online version of PPVC, which can timely update cached videos with the incremental arrival of events. One nice property of PPVC is that it can adapt to the change of video popularity very fast and is also applicable to Internet devices at different levels, ranging from the high-level servers that serve a large population of users to the low-level user devices ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , personal computers). Finally, we conduct extensive simulations with real traces and the results show that PPVC can always achieve the best video caching performance in terms of the hit rate on all levels of Internet devices, and quickly adapt to the dynamics of video requests.

CVC: A Collaborative Video Caching Framework Based on Federated Learning at the Edge

With the rapid development of Internet social media platforms and the popularization of smart terminal devices, video services such as short videos have surged. However, the massive amount of smart devices connected to the core network causes the increase of load on the backhaul link, which makes traditional cloud computing unable to meet the low-latency requirement of user for video services fully. To this end, we propose to implement proactive video caching at the edge to improve the quality of user experience. Thus, a novel collaborative video caching framework at the edge, CVC, is proposed. In this framework, we present a video request prediction model based on federated learning, aiming to reduce communication costs. Also, we design two methods to improve the hit rate and reduce latency, including a collaborative caching decision method ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CCD</i> ) and a collaborative service response method ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CSR</i> ). Under the video service scenario, the experimental results show that CVC can improve the cache hit rate and reduce the waiting delay of users compared with the traditional caching algorithms. Simultaneously, CVC can also reduce the overall system’s communication cost and caching cost.

Cooperative Multigroup Broadcast 360° Video Delivery Network: A Hierarchical Federated Deep Reinforcement Learning Approach

With the stringent requirement of receiving video from the unmanned aerial vehicle (UAV) from anywhere in the stadium of sports events and the significant-high per-cell throughput for video transmission to virtual reality (VR) users, a promising solution is a cell-free multi-group broadcast (CF-MB) network with cooperative reception and broadcast access-points (AP). To explore the benefit of broadcasting user-correlated decode-dependent video resources to spatially correlated VR users, the network should dynamically schedule the video and cluster APs into virtual cells for a different group of VR users with overlapped video requests. By decomposing the problem into scheduling and association sub-problems, we first introduce the conventional non-learning-based scheduling and association algorithms, and a centralized deep reinforcement learning (DRL) association approach based on the rainbow agent with a convolutional neural network (CNN) to generate decisions from observation. To reduce its complexity, we then decompose the association problem into multiple sub-problems, resulting in a networked-distributed Partially Observable Markov decision process (ND-POMDP). To solve it, we propose a multi-agent deep DRL algorithm. To jointly solve the coupled association and scheduling problems, we further develop a hierarchical federated DRL algorithm with scheduler as meta-controller, and association as the controller. Our simulation results show that our CF-MB network can effectively handle real-time video transmission from UAVs to VR users. Our proposed learning architecture is effective and scalable for a high-dimensional cooperative association problem with increasing APs and VR users. Also, our proposed algorithms outperform non-learning based methods with significant performance improvement.

Optimizing Video Caching at the Edge: A Hybrid Multi-Point Process Approach

It is always a challenging problem to deliver a huge volume of videos over the Internet. To meet the high bandwidth and stringent playback demand, one feasible solution is to cache video contents on edge servers based on predicted video popularity. Traditional caching algorithms (e.g., LRU, LFU) are too simple to capture the dynamics of video popularity, especially long-tailed videos. Recent learning-driven caching algorithms (e.g., DeepCache) show promising performance, however, such black-box approaches are lack of explainability and interpretability. Moreover, the parameter tuning requires a large number of historical records, which are difficult to obtain for videos with low popularity. In this paper, we optimize video caching at the edge using a white-box approach, which is highly efficient and also completely explainable. To accurately capture the evolution of video popularity, we develop a mathematical model called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HRS</i> model, which is the combination of multiple point processes, including Hawkes’ self-exciting, reactive and self-correcting processes. The key advantage of the HRS model is its explainability, and much less number of model parameters. In addition, all its model parameters can be learned automatically through maximizing the Log-likelihood function constructed by past video request events. Next, we further design an online HRS-based video caching algorithm. To verify its effectiveness, we conduct a series of experiments using real video traces collected from Tencent Video, one of the largest online video providers in China. Experiment results demonstrate that our proposed algorithm outperforms the state-of-the-art algorithms, with 15.5% improvement on average in terms of cache hit rate under realistic settings.

Video Content Placement At the Network Edge: Centralized and Distributed Algorithms

In the traditional video streaming service provisioning paradigm, viewers typically request video content through a central Content Delivery Network (CDN) server. However, because of the uncertain wide area network delays, the (remote) viewers usually suffer from long video streaming delay, which affects the quality of experience. Multi-Access Edge Computing (MEC) offers a way to shorten the video streaming delay by building small-scale cloud infrastructures at the network edge, which are in close proximity to the viewers. In this paper, we present novel centralized and distributed algorithms for the video content placement problem in MEC. In the proposed centralized video content placement algorithm, we leverage the Lyapunov optimization technique to formulate the video content placement problem as a series of one-time-slot optimization problems and apply an Alternating Direction Method of Multipliers (ADMM)-based method to solve each of them. We further devise a distributed Multi-Agent Reinforcement Learning (MARL)-based method with value decomposition mechanism and parallelization policy update method to solve the video content placement problem. The value Decomposition mechanism deals with the credit assignment among multiple agents, which promotes the cooperative optimization of the global target and reduces the frequency of information exchange. The parallelization of policy network can speed up the convergence process. Simulation results verify the effectiveness and superiority of our proposed centralized and distributed algorithms in terms of performance.

Toward Concurrent Video Multicast Orchestration for Caching-Assisted Mobile Networks

Multicasting does not work well for video applications such as video on demand, where individual users request video content at discrete and unpredictable moments. In this paper, we study a concurrent video multicast orchestration solution for the caching-assisted and centrally controlled mobile networks. The proposed solution strives to obtain rapid video prefetch and effective traffic reduction. By rapid video prefetch, user devices can cache required contents in advance, which can effectively improve the streaming fluency in unsteady wireless environments, especially the highly mobile vehicular environment. We introduce a maximum-rate concurrent multicast (MRCM) problem whose solution is intended to maximize the total video delivery rate by a concurrent multicast manner, and prove that the MRCM problem is NP-hard. Then, we propose a dynamic concurrent multicast tree construction algorithm and a global concurrent multicast tree optimization algorithm to approximately solve the MRCM problem in polynomial time. We also introduce a cache competition mechanism that strives to maximize the total cache hit rate when the cache space is insufficient. Performance evaluations show that compared with the existing cache-assisted multicast delivery solutions, the proposed solution significantly reduces the video delivery duration and the traffic stress of backhaul links.

Two-phase sample average approximation for video distribution strategy of edge computing in heterogeneous network

Cooperative edge computing provides a good platform for edge storage and computing at the same time. When a single edge server cannot efficiently provide video services, multiple edge servers are needed to share resources for collaborative storage and computing. Existing collaboration schemes often ignore the coupling between video caching and distribution, resulting in inefficient caching strategies. Therefore, for the scenario where multiple edge servers provide video services cooperatively, the content access delay in the video service is minimized in this paper based on the video caching strategy on the slow time scale and the video distribution strategy on the fast time scale. The problem is modeled as a random integer linear programming problem on dual time scales. Moreover, an algorithm is proposed based on the sample average approximation technique. The algorithm first designs the video caching strategy of the edge server based on the statistical information of the arrival of video requests on the slow time scale and the expected video distribution strategy. Then, the video distribution strategy is optimized based on the designed caching strategy and the video request situation on fast time scales. Finally, through the cooperation of the two time scales, the content access delay is minimized, and the simulation results verify the advantages of the proposed scheme in reducing content access delay and improving storage hit rate. The storage hit rate is increased by 7.6%; the total content access delay is increased by 17.42%; the number of edge servers and the arrival rate of video requests are ahead of other methods by 5%; and the transcoding time is shortened by 4.56 s. It fully verifies that the design of a more efficient multi-user computing offload strategy in this paper can solve the problems of computing power, bandwidth, delay Energy consumption and other bottlenecks are of practical significance.

A Novel Video Transmission Optimization Mechanism Based on Reinforcement Learning and Edge Computing

As we know, the video transmission traffic already constitutes 60% of Internet downlink traffic. The optimization of video transmission efficiency has become an important challenge in the network. This paper designs a video transmission optimization strategy that takes reinforcement learning and edge computing (TORE) to improve the video transmission efficiency and quality of experience. Specifically, first, we design the popularity prediction model for video requests based on the RL (reinforcement learning) and introduce the adaptive video encoding method for optimizing the efficiency of computing resource distribution. Second, we design a video caching strategy, which adopts EC (edge computing) to reduce the redundant video transmission. Last, simulations are conducted, and the experimental results fully demonstrate the improvement of video quality and response time.

FastTrack: Minimizing Stalls for CDN-Based Over-the-Top Video Streaming Systems

Traffic for internet video streaming has been rapidly increasing and is further expected to increase with the higher definition videos and IoT applications, such as 360 degree videos and augmented virtual reality applications. While efficient management of heterogeneous cloud resources to optimize the quality of experience is important, existing work in this problem space often left out important factors. In this paper, we present a model for describing a today’s representative system architecture for video streaming applications, typically composed of a centralized origin server and several CDN sites. Our model comprehensively considers the following factors: limited caching spaces at the CDN sites, allocation of CDN for a video request, choice of different ports from the CDN, and the central storage and bandwidth allocation. With the model, we focus on minimizing a performance metric, stall duration tail probability (SDTP), and present a novel, yet efficient, algorithm to solve the formulated optimization problem. The theoretical bounds with respect to the SDTP metric are also analyzed and presented. Our extensive simulation results demonstrate that the proposed algorithms can significantly improve the SDTP metric, compared to the baseline strategies. Small-scale video streaming system implementation in a real cloud environment further validates our results.

Game-based incentive mechanism for enabling edge video caching over passive optical networks

With the ever-increasing mobile video traffic, edge caching enabled passive optical networks (PONs), where storages are deployed on optical network units (ONUs), are becoming a desirable back-end for mobile backhaul (MBH). By caching videos on ONUs, video requests generated in MBH can be efficiently accommodated. Thus, many previous works focus on leveraging the caching capabilities of ONUs to maximize the video cache-hit ratio. However, it is largely ignored how to stimulate individual ONUs to contribute their storages for caching videos. In this paper, we propose a game-based incentive mechanism through which the content provider (CP) rewards ONUs for their storage-contributions. We firstly formulate a two-level Stackelberg game to capture the interaction between the CP and ONUs. At the first level, the CP determines rewarding policy to maximize its utility. At the second level, ONUs react with the amount of provided storages with the aim to cooperatively maximize the social utility or competitively maximize their respective utilities, and thereby two sub-games are further modeled to depict the cooperation and competition among ONUs. We then give the specific strategies of cooperative ONUs and competitive ONUs, respectively. We also show the existence of an equilibrium in the formulated Stackelberg game. The performance of the proposed incentive scheme is finally evaluated by simulations.

Analysis of a Cooperative Caching-Multicast Strategy in D2D-Aided Networks

Due to characteristics of video request, i.e., request-repeatability and group-orientation, we propose a cooperative caching-multicast strategy in typical Device-to-Device (D2D)-aided networks to improve the timeliness and space coverage of video services. First, based on the request cumulative time window (RCTW), a multicast-scale based caching resource allocation scheme is presented and videos whose corresponding multicast groups have larger probabilities to be non-empty are allocated more caching spaces. Second, a collaborative transmission scheme is presented, where base station opportunistically multicasts blocks of the requested video to group while failed receivers can obtain the missing blocks from successful receivers with the aid of D2D joint transmission. Finally, the tractable expressions for the average transmission coverage rate and the system average transmission delay are derived. The effectiveness of the proposed strategy when the video popularity distribution is in the general dip range is verified by numerical examples. Simulation results demonstrate that a reasonable RCTW can balance the relationship between the service probability of more users through one multicast transmission and the system transmission delay.

Discovering and Understanding Geographical Video Viewing Patterns in Urban Neighborhoods

Video accounts for a large proportion of traffic on the Internet. Understanding its geographical viewing patterns is extremely valuable for the design of Internet ecosystems for content delivery, recommendation and ads. While previous works have addressed this problem at coarse-grain scales (e.g., national), the urban-scale geographical patterns of video access have never been revealed. To this end, this article aims to investigate the problem that whether there exists distinct viewing patterns among the neighborhoods of a large-scale city. To achieve this, we need to address several challenges including unknown of patterns profiles, complicate urban neighborhoods, and comprehensive viewing features. The contributions of this article include two aspects. First, we design a framework to automatically identify geographical video viewing patterns in urban neighborhoods. Second, by using a dataset of two months real video requests in Shanghai collected from one major ISP of China, we make a rigorous analysis of video viewing patterns in Shanghai. Our study reveals the following important observations. First, there exists four prevalent and distinct patterns of video access behavior in urban neighborhoods, which are corresponding to four different geographical contexts: downtown residential, office, suburb residential and hybrid regions. Second, there exists significant features that distinguish different patterns, e.g., the probabilities of viewing TV plays at midnight, and viewing cartoons at weekends can distinguish the two viewing patterns corresponding to downtown and suburb regions.

Cooperative Content Caching in MEC-Enabled Heterogeneous Cellular Networks

Multimedia content delivery via the cellular infrastructure increases fast due to the very high volumes of mobile video traffic generated by the billions of end devices populating the mobile data network. A critical mass of mobile video content requests refers to the consumption of the same popular video content, which is consumed by different end terminals spanning small geographical regions. Such content requests place a great burden on the backhaul of content-agnostic cellular networks, which fail to exploit the correlation of video requests to decongest their backhaul links. This creates redundant retransmissions while fetching the same video content from a central server to the network edge, using the bandwidth-limited backhaul at peak-time periods. With the integration of multi-access edge computing (MEC) capabilities in 5G mobile cellular networks, mobile network operators can place popular video content closer to the network edge at off-peak time periods, predicting user requests exhibiting a high correlation for a given time interval over smaller geographical regions. In this paper, we investigate popular content placement in multi-tier heterogeneous cellular networks where the edge network infrastructure can cooperate to create content delivery (and placement) clusters to effectively serve correlated video requests. To this end, we model the cooperative content placement problem using the multiple knapsack problem (MKP) formulation and present an exact (optimal) bound-and-bound strategy to solve it. The performance of the proposed strategy is evaluated in-depth using extensive system-level simulations and is compared against that of other state-of-the-art algorithms. Valuable design guidelines and key performance trade-offs are discussed, paving the way towards cluster-based cooperative caching in MEC-enabled cellular network setups.