Multipath Scheduling Algorithm for UAV Video Streaming

CAO Changlong; LI Lingzhi; SHI Lianmin; ZHAO Qingyue

doi:10.11999/JEIT260002

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CAO Changlong, LI Lingzhi, SHI Lianmin, ZHAO Qingyue. Multipath Scheduling Algorithm for UAV Video Streaming[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260002

Citation:

CAO Changlong, LI Lingzhi, SHI Lianmin, ZHAO Qingyue. Multipath Scheduling Algorithm for UAV Video Streaming[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260002

CAO Changlong, LI Lingzhi, SHI Lianmin, ZHAO Qingyue. Multipath Scheduling Algorithm for UAV Video Streaming[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260002

Citation:

CAO Changlong, LI Lingzhi, SHI Lianmin, ZHAO Qingyue. Multipath Scheduling Algorithm for UAV Video Streaming[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260002

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Multipath Scheduling Algorithm for UAV Video Streaming

doi: 10.11999/JEIT260002 cstr: 32379.14.JEIT260002

1.
School of Computer Science and Technology, Soochow University, Suzhou 215008, China
2.
School of Future Science and Engineering, Soochow University, Suzhou 215222, China
3.
Key Laboratory of General Artificial Intelligence and Large Models in Provincial Universities, Soochow University, Suzhou 215222, China
4.
Suzhou Xiyu Information Technology Co., Ltd, Suzhou 215164, China

Funds: The National Natural Science Foundation of China (62072321), The National Key R&D Program of China (2023YFB4503100), The Science and Technology Program of Jiangsu Province (BZ2024062), The Natural Science Foundation of the Jiangsu Higher Education Institutions of China (22KJA520007), Suzhou Planning Project of Science and Technology (SNG2025010, 2023SS03, SYG2024144, SYG2025129)

Received Date: 2026-01-04
Accepted Date: 2026-04-09
Rev Recd Date: 2026-04-02

Available Online: 2026-04-30

Abstract

Abstract

Objective With the rapid growth of the low-altitude economy, Unmanned Aerial Vehicle (UAV) technology has been widely used in emergency rescue, disaster monitoring, urban security, and other applications. In these scenarios, stable, low-latency, and high-fidelity video backhaul is critical for task execution. Multipath transport protocols can improve Quality of Experience (QoE) through bandwidth aggregation, providing an effective basis for UAV video streaming. However, under dynamic and heterogeneous network conditions, the performance of multipath transport protocols depends strongly on the design of multipath scheduling algorithms. Existing heuristic schedulers use predefined rules to reduce head-of-line blocking and inter-path load imbalance, but their adaptability remains limited in highly dynamic environments. Learning-based schedulers can learn the mapping between network states and scheduling rewards from real-time feedback, enabling adaptive performance optimization. However, most existing learning-based schedulers are designed for general network scenarios. They are not optimized for UAV networks, and their ability to guarantee QoE has not been fully validated. A multipath scheduling algorithm tailored to UAV video streaming is therefore needed to better exploit the performance potential of multipath transport protocols. Methods To address the dynamic and heterogeneous challenges of UAV video streaming, this paper proposes NeuroFly, a multipath scheduling framework based on the NeuralUCB algorithm. In NeuroFly, multipath traffic scheduling is formulated as a Contextual Multi-Armed Bandit (CMAB) problem. The context space is constructed by integrating path state information, video encoding features, and UAV mobility parameters, which jointly characterize the current transmission environment. In the action space, a frame-priority-driven redundant transmission mechanism is proposed. Video frames are assigned different frame priorities according to decoding dependencies, and differentiated redundancy strategies are used to improve the probability of successful video-frame delivery. A multi-objective reward function is further designed to guide policy learning and support adaptive optimization under dynamic and heterogeneous network conditions. In addition, a context monitoring mechanism is integrated into NeuroFly to handle abrupt environmental changes caused by high UAV mobility. This mechanism detects context distribution shifts and triggers a two-stage restart strategy. A soft restart is activated when gradual context drift is detected, removing outdated historical experience. A hard restart is performed under abrupt context changes by clearing the experience replay buffer and reinitializing model parameters, allowing learning to restart under a new distribution. Results and Discussions The proposed NeuroFly framework is evaluated in both simulation and field environments. First, Mininet-WiFi is used to simulate realistic UAV network environments and evaluate overall QoE performance. The results (Fig. 4) show that, compared with state-of-the-art heuristic and learning-based schedulers, NeuroFly achieves broad performance gains by fully using aggregated multipath bandwidth. Specifically, the 99th-percentile latency is reduced by 19.9%～51.0%, the average video frame rate is increased by up to 24.6%, image structural similarity is improved by up to 49.2%, and the buffering time ratio is reduced by 13.4%～77.6%. These results demonstrate the strong ability of NeuroFly to guarantee QoE. Field experiments (Fig. 6) further confirm that NeuroFly provides favorable optimization in real UAV operation scenarios. Compared with mainstream transport solutions widely deployed in production environments, NeuroFly achieves better real-time transmission performance and shows strong practical applicability for future large-scale UAV deployment. Conclusions This paper addresses network dynamics, path heterogeneity, and time-varying transmission conditions in UAV video streaming over multipath transport protocols. An intelligent multipath scheduling framework, NeuroFly, is proposed based on the NeuralUCB algorithm. In this framework, multipath traffic scheduling is modeled as a CMAB problem. Through the design of the context space, action space, and multi-objective reward function, online learning and adaptive optimization of traffic allocation policies are achieved. To further improve robustness under severe environmental changes, a lightweight context monitoring mechanism is introduced to detect context distribution drift and restart the learning process when needed. Systematic evaluations are conducted on both simulation platforms and real UAV operation environments. The simulation results show that NeuroFly achieves consistent improvements across QoE metrics compared with state-of-the-art heuristic and learning-based schedulers. The field results further indicate that NeuroFly provides reliable guarantees in actual UAV operation scenarios when compared with mature solutions that have been widely deployed in production environments. These results validate the practicality, robustness, and engineering feasibility of NeuroFly, and suggest its potential for large-scale deployment in UAV applications that are sensitive to real-time video quality, including emergency response, power inspection, agricultural monitoring, and logistics delivery.

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