MEC‐enabled video streaming in device‐to‐device networks

Zhang, Xuguang; Lin, Huangda; Chen, Miao; Kong, Bin; Wang, Lei

doi:10.1049/iet-com.2019.1198

Cited by 8 publications

(6 citation statements)

References 28 publications

(29 reference statements)

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“…SVC coding follows a frame structure similar to AVC on GOP hierarchy terms but includes a parallel structure. The parallel structure is based on the number of ELs which depends on BL [21,22]. Figure 2 shows the SVC hierarchy I-B for two layers with a GOP size of 8 frames; it follows an IBBBBBBB pattern.…”

Section: Video Codingmentioning

confidence: 99%

ASViS: A Protocol for Efficient Video Streaming Using SVC and Deadline-based Criteria.

Peña-Ancavil

Estevez

Orchard

et al. 2023

Preprint

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Multimedia video streaming is the most used data consumption service on the Internet. Maintaining a data flow rate that provides the best video quality possible without overflowing the pipeline is challenging. Dynamic Adaptive Streaming over HTTP (DASH) is a commonly used protocol that sends video data in small packages called chunks. However, this technique can lead to video buffering issues and video pausing due to an empty video buffer. Adaptive Scalable Video Streaming (ASViS) is a more versatile protocol that uses scalable video coding, a flow-controlled user datagram protocol (UDP), and deadline-based criteria to manage the buffered data. Like transmission control protocol (TCP), ASViS adjusts the flow based on available bandwidth, and it can discard data based on video frame deadlines to prevent outdated data transmission. ASViS is compliant with RFC 8085, preventing it from overwhelming the Internet. The scalability feature allows ASViS to have the highest image quality possible in every frame allowed by SVC with the data layers available. A theoretical model is presented that predicts ASViS behavior under different network conditions. This work also shows how different parameters affect its performance and how it can be easily configured to optimize it.

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Section: Video Codingmentioning

confidence: 99%

ASViS: A Protocol for Efficient Video Streaming Using SVC and Deadline-based Criteria.

Peña-Ancavil

Estevez

Orchard

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The encoded live videos from the video content server are transmitted to the MEC servers from the video content server located in the cloud. An edge computing algorithm deployed at MEC servers [186] located at BSs uses some input parameters, including the link status estimation and rate assignment, to calculate the optimized aggregation goodput performance of the input live video traic. Instead of caching multiple versions of a video with diferent resolutions, MEC servers deploy the SVC result to achieve high video transmission eiciency.…”

Section: Hybrid Solutionsmentioning

confidence: 99%

“…These types of users can either receive direct packets from the serving MEC server or from other users under the assumption that the devices in the D2D network can access both cellular and D2D networks. In contrast to [40], the relaying hop in [186] is limited to one hop. This proposed model was evaluated through goodput (i.e., the amount of data received by the receiving end within the efective time), average end-to-end delay, average efective loss rate, and average QoE, which is the relational expression of the former.…”

Section: Hybrid Solutionsmentioning

confidence: 99%

A Contemporary Survey on Live Video Streaming from a Computation-Driven Perspective

Dao

Tran

et al. 2022

ACM Comput. Surv.

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Live video streaming services have experienced significant growth since the emergence of social networking paradigms in recent years. In this scenario, adaptive bitrate streaming communications transmitted on web protocols provide a convenient and cost-efficient facility to serve various multimedia platforms over the Internet. In these communication models, video content is delivered optimally, possibly transcoded, edited automatically, and cached temporarily by network elements along the path. To this end, the computational capabilities of various network elements are considered as major resources to be optimized for service quality improvements. This paper provides a contemporary survey of cutting-edge live video streaming studies from a computation-driven perspective. First, an overview of the global standards, system architectures, and streaming protocols is presented. Next, hierarchical computation-driven models of live video streaming are anatomized, including cloud-, edge-, and peer-to-peer-based solutions. Cutting-edge studies are then reviewed to discover the advances they have made in improving system performance in multiple aspects. Finally, open challenges are presented to direct future research in this field.

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“…Math et al 41 used deep learning for traffic caching, adjusted communication traffic according to resource utilization, and controlled forwarding traffic depending on resource availability. Zhang et al 42 constructed a novel MEGA architecture to solve the problem of insufficient cache storage space, serving delay‐sensitive real‐time video streaming in heterogeneous wireless networks that support the edge.…”

Section: The Related Workmentioning

confidence: 99%

A differential privacy‐preserving deep learning caching framework for heterogeneous communication network systems

Wang

Zhang

Xia

et al. 2022

Int J of Intelligent Sys

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Caching and artificial intelligence technology are means to improve the content transmission rate and solve the delay problem in heterogeneous communication networks, which provide an important technical basis for telemedicine diagnosis, disease monitoring, and other medical and healthcare fields. However, since the data used for caching recommendations and mining usually includes a lot of sensitive information, collecting these data will inevitably cause users to worry about personal privacy security. This paper proposes a differential privacy‐preserving deep learning caching framework (DP‐DLCF) for heterogeneous communication networks. First, the multivariate randomized response mechanism is used to perturb the mobile client's sensitive information while introducing the least square method to balance the data privacy and utility, and realize the adaptive allocation of the privacy budget. Then use selective integrated learning methods to further improve the local cache accuracy of the neural network training model. Therefore, the framework not only improves the prediction accuracy of the cache network but also solves the problem of privacy leakage in data sharing. Finally, four simulation examples verify the DP‐DLCF framework's effectiveness.

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MEC‐enabled video streaming in device‐to‐device networks

Cited by 8 publications

References 28 publications

ASViS: A Protocol for Efficient Video Streaming Using SVC and Deadline-based Criteria.

ASViS: A Protocol for Efficient Video Streaming Using SVC and Deadline-based Criteria.

A Contemporary Survey on Live Video Streaming from a Computation-Driven Perspective

A differential privacy‐preserving deep learning caching framework for heterogeneous communication network systems

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