Logan: Loss-tolerant Live Video Analytics System

Cloud-based live video analytics with tight latency bound is gaining importance to support emerging applications such as UAVs and augmented reality. However, existing systems often struggle to meet stringent latency constraints under fluctuating network conditions with packet losses and late-arriving packets. We propose a loss-tolerant live video analytics system called Logan, which effectively accepts packet losses while maintaining high accuracy by utilizing the inherent resilience in DNNs. We design i) Codec-aware Inpainting, which accurately recovers the frame error from packet losses ii) Fast-Forward Recovery that prevents the remaining un-recovered error from propagating over future frames indefinitely. Our results show a 3× improvement (33.2%→99.9%) in SLO satisfaction rate compared to the reliable transmission scheme with <1% accuracy drop under a 5% packet loss rate.

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Publication Details

Published
2024-12-04
DOI
https://doi.org/10.1145/3636534.3690695
Citations
3
Primary Topic
Image and Video Quality Assessment
Type
article
Field-Weighted Citation Impact
0.75

Funders

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article

Logan: Loss-tolerant Live Video Analytics System

Jingyu Lee, Juheon Yi, Park Kyoungsoo, Minkyung Jeong et al.
3 citations
Image and Video Quality Assessment
0.75
article

Logan: Loss-tolerant Live Video Analytics System

Jingyu Lee, Juheon Yi, Park Kyoungsoo, Minkyung Jeong, Kichang Yang, Youngki Lee
article en
3 citations

Abstract

Cloud-based live video analytics with tight latency bound is gaining importance to support emerging applications such as UAVs and augmented reality. However, existing systems often struggle to meet stringent latency constraints under fluctuating network conditions with packet losses and late-arriving packets. We propose a loss-tolerant live video analytics system called Logan, which effectively accepts packet losses while maintaining high accuracy by utilizing the inherent resilience in DNNs. We design i) Codec-aware Inpainting, which accurately recovers the frame error from packet losses ii) Fast-Forward Recovery that prevents the remaining un-recovered error from propagating over future frames indefinitely. Our results show a 3× improvement (33.2%→99.9%) in SLO satisfaction rate compared to the reliable transmission scheme with <1% accuracy drop under a 5% packet loss rate.

Seoul National University (KR), Nokia (United Kingdom) (GB)
National Research Foundation of Korea
Openalex Percentile: Top 26%
Image and Video Quality Assessment
0.75
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