# BiSwift: Bandwidth Orchestrator for Multi-Stream Video Analytics on Edge

arXiv: https://arxiv.org/abs/2312.15740 · HTML: https://mlsanity.com/paper/2312.15740/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 60 references that are in the map.
- First posted on arXiv 25 Dec 2023.

## Builds on (most cited first, 14 of 14)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network](https://mlsanity.com/paper/1803.08664/) (2018)
- [Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond](https://mlsanity.com/paper/2101.11517/) (2021)
- [SwiftNet: Real-time Video Object Segmentation](https://mlsanity.com/paper/2102.04604/) (2021)
- [Focus: Querying Large Video Datasets with Low Latency and Low Cost](https://mlsanity.com/paper/1801.03493/) (2018)
- [NoScope: Optimizing Neural Network Queries over Video at Scale](https://mlsanity.com/paper/1703.02529/) (2017)
- [Scaling Video Analytics on Constrained Edge Nodes](https://mlsanity.com/paper/1905.13536/) (2019)
- [GEMEL: Model Merging for Memory-Efficient, Real-Time Video Analytics at the Edge](https://mlsanity.com/paper/2201.07705/) (2022)
- [FAST: A Framework to Accelerate Super-Resolution Processing on Compressed Videos](https://mlsanity.com/paper/1603.08968/) (2016)
- [AccMPEG: Optimizing Video Encoding for Video Analytics](https://mlsanity.com/paper/2204.12534/) (2022)
- [AccDecoder: Accelerated Decoding for Neural-enhanced Video Analytics](https://mlsanity.com/paper/2301.08664/) (2023)

## Built on by (most cited first, 1 of 1)

- [Rethinking Streaming-Perception Evaluation on Heterogeneous Edge Platforms](https://mlsanity.com/paper/2610.05578/) (2026)

## Abstract (arXiv)

High-definition (HD) cameras for surveillance and road traffic have experienced tremendous growth, demanding intensive computation resources for real-time analytics. Recently, offloading frames from the front-end device to the back-end edge server has shown great promise. In multi-stream competitive environments, efficient bandwidth management and proper scheduling are crucial to ensure both high inference accuracy and high throughput. To achieve this goal, we propose BiSwift, a bi-level framework that scales the concurrent real-time video analytics by a novel adaptive hybrid codec integrated with multi-level pipelines, and a global bandwidth controller for multiple video streams. The lower-level front-back-end collaborative mechanism (called adaptive hybrid codec) locally optimizes the accuracy and accelerates end-to-end video analytics for a single stream. The upper-level scheduler aims to accuracy fairness among multiple streams via the global bandwidth controller. The evaluation of BiSwift shows that BiSwift is able to real-time object detection on 9 streams with an edge device only equipped with an NVIDIA RTX3070 (8G) GPU. BiSwift improves 10%~21% accuracy and presents 1.2~9× throughput compared with the state-of-the-art video analytics pipelines.