Proactive Video Chunks Caching and Processing for Latency and Cost Minimization in Edge Networks

Baccour, Emna; Erbad, Aiman; Mohamed, Amr; Bilal, Kashif; Guizani, Mohsen

doi:10.1109/wcnc.2019.8885906

Cited by 16 publications

(10 citation statements)

References 8 publications

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“…To this aim, we assume that for the first GOP, the users' requests for the various 360 o videos are decided by sampling a Zipfian distribution [39] with shape parameter η v = 1. Hence, the probability of a 360 o video to be selected is given by: To capture the evolution of the popularity of the 360 o videos with the time, inspired by [40], we assume that the probability of a user to stop watching a 360 o video at the GOP g ∈ {2, . .…”

Section: A Simulation Setupmentioning

confidence: 99%

“…, G} follows a Weibull distribution. Further, following assumptions made in [40], the 360 o videos that comprise the content library belong to one of 14 video categories, e.g., News, Sports, Education, etc., with equal probability. The Weibull distribution parameters for each video category can be found in [40].…”

Section: A Simulation Setupmentioning

confidence: 99%

“…Further, following assumptions made in [40], the 360 o videos that comprise the content library belong to one of 14 video categories, e.g., News, Sports, Education, etc., with equal probability. The Weibull distribution parameters for each video category can be found in [40]. For each GOP, when users stop watching a 360 o video, the probability of selecting a different video to watch follows again a Zipfian distribution with the same shape parameter as the original video dataset.…”

Section: A Simulation Setupmentioning

confidence: 99%

See 2 more Smart Citations

Tile-Based Edge Caching for 360° Live Video Streaming

Maniotis

Thomos

2021

IEEE Trans. Circuits Syst. Video Technol.

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360 o video is becoming an increasingly popular technology on commercial social platforms and vital part of emerging Virtual Reality/Augmented Reality (VR/AR) applications. However, the delivery of 360 o video content in mobile networks is challenging because of its size. The encoding of 360 o video into multiple quality layers and tiles and edge cacheassisted video delivery have been proposed as a remedy to the excess bandwidth requirements of 360 o video delivery systems. Existing works using the above tools have shown promising performance for Video-on-Demand (VoD) 360 o delivery, but they cannot be straightforwardly extended in a live-streaming setup. Motivated by the above, we study edge cache-assisted 360 o live video streaming to increase the overall quality of the delivered 360 o videos to users and reduce the service cost. We employ Long Short-Term Memory (LSTM) networks to forecast the evolution of the content requests and prefetch content to caches. To further enhance the delivered video quality, users located in the overlap of the coverage areas of multiple Small Base Stations (SBSs) are allowed to receive data from any of these SBSs. We evaluate and compare the performance of our algorithm with Least Frequently Used (LFU), Least Recently Used (LRU), and First In First Out (FIFO) algorithms. The results show the superiority of the proposed approach in terms of delivered video quality, cache-hitratio and backhaul link usage. Index Terms-Tile-encoding, 360 o live video streaming, edgecaching, LSTM networks.

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Section: A Simulation Setupmentioning

confidence: 99%

Section: A Simulation Setupmentioning

confidence: 99%

Section: A Simulation Setupmentioning

confidence: 99%

See 1 more Smart Citation

Tile-Based Edge Caching for 360° Live Video Streaming

Maniotis

Thomos

2021

IEEE Trans. Circuits Syst. Video Technol.

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“…On the other hand, deploying cache in the edge network can enhance content delivery networks (CDN) and reducing the data traffic caused by a large number of repeated content requests. By analyzing popularity and proactively caching popular content from the core network to the network edge in advance, the subsequent requests for the same content can be obtained in the edge nodes without duplicate transmissions from remote core network, which can reduce transmission delay and alleviate the pressure of backhaul link and core network [15]. Edge caching has been widely studied due to the advantages of QOE improvement and energy saving.…”

Section: Introductionmentioning

confidence: 99%

Resource Allocation for Heterogeneous Service in Green Mobile Edge Networks Using Deep Reinforcement Learning

2021

KSII TIIS

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The requirements for powerful computing capability, high capacity, low latency and low energy consumption of emerging services, pose severe challenges to the fifth-generation (5G) network. As a promising paradigm, mobile edge networks can provide services in proximity to users by deploying computing components and cache at the edge, which can effectively decrease service delay. However, the coexistence of heterogeneous services and the sharing of limited resources lead to the competition between various services for multiple resources. This paper considers two typical heterogeneous services: computing services and content delivery services, in order to properly configure resources, it is crucial to develop an effective offloading and caching strategies. Considering the high energy consumption of 5G base stations, this paper considers the hybrid energy supply model of traditional power grid and green energy. Therefore, it is necessary to design a reasonable association mechanism which can allocate more service load to base stations rich in green energy to improve the utilization of green energy. This paper formed the joint optimization problem of computing offloading, caching and resource allocation for heterogeneous services with the objective of minimizing the on-grid power consumption under the constraints of limited resources and QoS guarantee. Since the joint optimization problem is a mixed integer nonlinear programming problem that is impossible to solve, this paper uses deep reinforcement learning method to learn the optimal strategy through a lot of training. Extensive simulation experiments show that compared with other schemes, the proposed scheme can allocate resources to heterogeneous service according to the green energy distribution which can effectively reduce the traditional energy consumption.

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“…Future 5G and 6G networks aim to provide high-speed access to end devices while increasing traffic load of transit networks and workload of cloud data centers [1], [2]. CDNs and micro-data centers have been utilized to distribute the content with respect to geographic regions, hence, partially alleviating the issue of latency [3]. It is estimated that mobile data traffic will increase 500-folds in the next decade with a mobile user downloading approximately 1 terabyte of data in 2020 [4].…”

Section: Introductionmentioning

confidence: 99%

Applying Machine Learning Techniques for Caching in Edge Networks: A Comprehensive Survey

Shuja,

Bilal,

Alasmary

et al. 2020

Preprint

Self Cite

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Edge networks provide access to a group of proximate users who may have similar content interests. Caching popular content at the edge networks leads to lower latencies while reducing the load on backhaul and core networks with the emergence of high-speed 5G networks. Edge networks are complex and dynamic. User mobility, preferences, and content popularity are the dominant dynamic features of the edge networks. Temporal and social features of content, such as the number of views and likes are applied to estimate the popularity of content from a global perspective. However, such estimates may not be mapped to an edge network with particular social and geographic characteristics. In edge networks, machine learning techniques can be applied to predict content popularity based on user preferences, user mobility based on user location history, cluster users based on similar content interests, and optimize cache placement and replacement strategies provided a set of constraints and predictions about the state of the network. These applications of machine learning can help identify relevant content for an edge network to lower latencies and increase cache hits. This article surveys the application of machine learning techniques for caching content in edge networks. We survey recent state-of-the-art literature and formulate a comprehensive taxonomy based on (a) machine learning technique (application and objective), (b) caching strategy (policy, location, and objective), and edge network (type and delivery strategy). We further survey supporting concepts for optimal edge caching decisions that require the application of machine learning. These supporting concepts are social-awareness, popularity prediction, and community detection in edge networks. A comparative analysis of the state-of-the-art literature is presented with respect to the parameters identified in the taxonomy. Moreover, we debate research challenges and future directions for optimal caching decisions and the application of machine learning towards caching in edge networks.

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Proactive Video Chunks Caching and Processing for Latency and Cost Minimization in Edge Networks

Cited by 16 publications

References 8 publications

Tile-Based Edge Caching for 360° Live Video Streaming

Tile-Based Edge Caching for 360° Live Video Streaming

Resource Allocation for Heterogeneous Service in Green Mobile Edge Networks Using Deep Reinforcement Learning

Applying Machine Learning Techniques for Caching in Edge Networks: A Comprehensive Survey

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