{"title":"Joint Encoding and Enhancement for Low-Light Video Analytics in Mobile Edge Networks","authors":"Yuanyi He;Peng Yang;Tian Qin;Jiawei Hou;Ning Zhang","doi":"10.1109/TMC.2024.3514214","DOIUrl":null,"url":null,"abstract":"In this paper, we present our design and analysis of a Joint Encoding and Enhancement (JEE) system for low-light video analytics in mobile edge networks. First, it is observed that, relying solely on a single pipeline for encoding and enhancement of mobile videos proves insufficient, because of the fluctuations in end-edge bandwidth and computing resources. Therefore, two distinct pipelines are introduced in the JEE system, namely, the encode-decode-enhance pipeline and the enhance-encode-decode pipeline. We then characterize the relationship of accuracy, transmission overhead, and computing overhead of these two pipelines through extensive experiments. Considering the significant demands of transmission and computing for low-light videos, we formulate an optimization problem to strike a balance between accuracy and delay, where the available end-edge bandwidth and computing resources are unknown in advance. To solve this mixed-integer nonlinear programming problem, we propose an algorithm based on online gradient descent, enabling adaptive pipeline selection and joint encoding and enhancement configuration. Theoretical analysis indicates that the proposed algorithm achieves sub-linear dynamic regret, highlighting its capability to the accuracy improvement and delay reduction in online environments. Experimental comparison against baselines demonstrates that, JEE can achieve up to a 27.32% increase in accuracy and a 26.18% reduction in delay.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"3330-3345"},"PeriodicalIF":7.7000,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Mobile Computing","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10787093/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, we present our design and analysis of a Joint Encoding and Enhancement (JEE) system for low-light video analytics in mobile edge networks. First, it is observed that, relying solely on a single pipeline for encoding and enhancement of mobile videos proves insufficient, because of the fluctuations in end-edge bandwidth and computing resources. Therefore, two distinct pipelines are introduced in the JEE system, namely, the encode-decode-enhance pipeline and the enhance-encode-decode pipeline. We then characterize the relationship of accuracy, transmission overhead, and computing overhead of these two pipelines through extensive experiments. Considering the significant demands of transmission and computing for low-light videos, we formulate an optimization problem to strike a balance between accuracy and delay, where the available end-edge bandwidth and computing resources are unknown in advance. To solve this mixed-integer nonlinear programming problem, we propose an algorithm based on online gradient descent, enabling adaptive pipeline selection and joint encoding and enhancement configuration. Theoretical analysis indicates that the proposed algorithm achieves sub-linear dynamic regret, highlighting its capability to the accuracy improvement and delay reduction in online environments. Experimental comparison against baselines demonstrates that, JEE can achieve up to a 27.32% increase in accuracy and a 26.18% reduction in delay.
期刊介绍:
IEEE Transactions on Mobile Computing addresses key technical issues related to various aspects of mobile computing. This includes (a) architectures, (b) support services, (c) algorithm/protocol design and analysis, (d) mobile environments, (e) mobile communication systems, (f) applications, and (g) emerging technologies. Topics of interest span a wide range, covering aspects like mobile networks and hosts, mobility management, multimedia, operating system support, power management, online and mobile environments, security, scalability, reliability, and emerging technologies such as wearable computers, body area networks, and wireless sensor networks. The journal serves as a comprehensive platform for advancements in mobile computing research.