A Survey on Mobile Edge Computing for Video Streaming: Opportunities and Challenges

Khan, Muhammad Salman; Baccour, Emna; Chkirbene, Zina; Erbad, Aiman; Hamila, Ridha; Hamdi, Mounir; Gabbouj, Moncef

doi:10.1109/access.2022.3220694

Cited by 32 publications

(23 citation statements)

References 204 publications

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“…According to studies [8,9], autonomous vehicles typically leverage vehicle-to-infrastructure (V2I) communication technology that allows the vehicles to offload the task of sensor data processing to a dedicated server. The server could be placed on the edge using the 5G MEC [10] or in a cloud with higher computational resources [11]. V2I communications are carried out using the upload / download path [5].…”

Section: V2i Communicationmentioning

confidence: 99%

“…C-V2X qualifies to support advanced applications [23,24], such as collective perception [25]. Processing of perception sensor data using the onboard vehicle computer might not always be an option due to the limited computational power [11]. However, collective perception using the V2X service allows cars to potentially offload sensory data to edge and cloud for resource-intensive computations [26,9].…”

Section: Perception Using C-v2x Communicationmentioning

confidence: 99%

“…However, collective perception using the V2X service allows cars to potentially offload sensory data to edge and cloud for resource-intensive computations [26,9]. Perception data processing in MEC or the cloud appears to be a viable option for autonomous driving, which has been the subject of many studies [5,11,12,9]. However, the perception of surrounding objects requires real-time detection, which demands rapid processing and low latency.…”

Section: Perception Using C-v2x Communicationmentioning

confidence: 99%

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Vehicle-to-Infrastructure Communication for Real-Time Object Detection in Autonomous Driving

Hawlader

Robinet

Frank

2023

2023 18th Wireless on-Demand Network Systems and Services Conference (WONS)

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Environmental perception is a key element of autonomous driving because the information received from the perception module influences core driving decisions. An outstanding challenge in real-time perception for autonomous driving lies in finding the best trade-off between detection quality and latency. Major constraints on both computation and power have to be taken into account for real-time perception in autonomous vehicles. Larger object detection models tend to produce the best results, but are also slower at runtime. Since the most accurate detectors cannot run in real-time locally, we investigate the possibility of offloading computation to edge and cloud platforms, which are less resource-constrained. We create a synthetic dataset to train object detection models and evaluate different offloading strategies. Using real hardware and network simulations, we compare different trade-offs between prediction quality and end-to-end delay. Since sending raw frames over the network implies additional transmission delays, we also explore the use of JPEG and H.265 compression at varying qualities and measure their impact on prediction metrics. We show that models with adequate compression can be run in real-time on the cloud while outperforming local detection performance.

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Section: V2i Communicationmentioning

confidence: 99%

Section: Perception Using C-v2x Communicationmentioning

confidence: 99%

Section: Perception Using C-v2x Communicationmentioning

confidence: 99%

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Vehicle-to-Infrastructure Communication for Real-Time Object Detection in Autonomous Driving

Hawlader

Robinet

Frank

2023

2023 18th Wireless on-Demand Network Systems and Services Conference (WONS)

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“…However, the aforementioned applications bring new challenges to the existing mobile networks. To meet the unprecedented traffic demand and to overcome the challenges, the computing and the caching capabilities at the edge of networks are considered as a promising solution to yield significant gains for both users and operators [3], [4]. The idea of caching was first introduced in computer systems for describing a memory with small capacity but with fast access requirement.…”

Section: Introductionmentioning

confidence: 99%

Content-Aware Transmission in UAV-Assisted Multicast Communication

Zhang

Xiong

et al. 2023

IEEE Trans. Wireless Commun.

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To alleviate the explosive growth of data traffic caused by the increased use of smart devices, new transmission techniques are needed to increase the utilization of limited bandwidth resources and for providing high transmission rates in future wireless networks. On the other hand, due to their flexibility and autonomy, unmanned aerial vehicles (UAVs) are considered as a potential candidate to support ubiquitous connectivity and operate as flying base stations, where the deployment of UAVs can affect the quality of experience (QoE) of users. Hence, in this paper, we employ UAVs as aerial base stations to transmit data to ground users (GUs) via air to ground (A2G) communication links, where we show how content awareness can help improve the data rate. Specifically, we design two contentsharing (CS) data transmission schemes to improve the average data rate of the GUs. Additionally, two UAV deployment strategies, namely the fixed-point deployment scheme and traversesearch deployment scheme, are proposed based on the proposed CS transmission schemes. The simulation results demonstrate that our proposed data transmission schemes combined with their proposed deployment schemes outperform the traditional transmission scheme by 26 bits/s/Hz and 51 bits/s/Hz, respectively.

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“…Separate sections are devoted to cutting-edge contributions in cooperative device-todevice (D2D) communication and machine learning. MEC-assisted video streaming is correlated and easily adaptable to the relevant use cases (3) . Insufficient uplink bandwidth is an important factor that influences the quality of live video transmissions.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Latency and Bandwidth for Edge-Based Short Length in Edge Computing Devices for Short Ranges and Long Ranges

Bharadwaj¹,

Barman²,

Saha³

2023

IJST

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Objectives:To propose an IoT-based design in an edge computing environment for short ranges and long ranges of real-time data streaming to the client. Methods: Multimedia data is used in this work. Here we have used Opnet for creating a physical architecture and to analyze the quality-of-service parameters like delay, throughput, and jitter for different Ad-hoc routing protocols like Ad-hoc on-Demand Distance vector routing, Dynamic source routing, Optimized Link State Routing, Temporally ordered routing algorithm and Geographic Routing Protocol for edge-based short length in edge computing devices for short ranges and long ranges. Riverbed academic modeler (formerly known as OPNET) has been used as a simulation tool to analyze the latency and bandwidth for edge computing under the pre-configured set-up. The simulation is accomplished using MATLAB to find the fluctuation of the delay, throughput, and jitter for edge-based short length in edge computing devices for short ranges and long ranges by applying the datasets in the edge computing environment. Findings: The results we got through simulations from different routing protocols like AODV, DSR, OLSR, TORA, and GRP of delay, throughput, and jitter shows that the proposed system strengthens the existing cloud-based system, leading to greater QoS.AODV surpasses all other protocols with a minimal delay of 0.24 msec and OLSR achieves the best performance with an average delay of 0.3 msec, better throughput of 180 bits/sec, and a load of 1410 bits/sec for the three key parameters of latency, throughput, and jitter. Novelty: The study compares the performance of different routing protocols and shows a reduction in the delay of 0.24 msec with respect to time as compared to other existing works. The proposed research is designed for a media streaming application based on the mobile nodes regarded as an edge devices for https://www.indjst.org/ different QoS parameters like throughput, delay, and load, and by comparing the existing work we found that AODV outperforms all other routing protocols with a minimum delay.

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A Survey on Mobile Edge Computing for Video Streaming: Opportunities and Challenges

Cited by 32 publications

References 204 publications

Vehicle-to-Infrastructure Communication for Real-Time Object Detection in Autonomous Driving

Vehicle-to-Infrastructure Communication for Real-Time Object Detection in Autonomous Driving

Content-Aware Transmission in UAV-Assisted Multicast Communication

Analysis of Latency and Bandwidth for Edge-Based Short Length in Edge Computing Devices for Short Ranges and Long Ranges

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