面向无人机视频流传输的多路径调度算法

曹昌龙; 李领治; 施连敏; 赵庆越

doi:10.11999/JEIT260002

面向无人机视频流传输的多路径调度算法

doi: 10.11999/JEIT260002 cstr: 32379.14.JEIT260002

曹昌龙¹,
李领治^{2, 3, ,},
施连敏⁴,
赵庆越¹

1.
苏州大学计算机科学与技术学院苏州 215008
2.
苏州大学未来科学与工程学院苏州 215222
3.
通用人工智能与大模型江苏省高校重点实验室苏州 215222
4.
苏州析羽信息科技有限公司苏州 215164

基金项目: 国家自然科学基金(62072321)，国家重点研发计划(2023YFB4503100)，江苏省科技计划项目(BZ2024062)，江苏省高等学校自然科学研究项目(22KJA520007)，苏州市科技计划项目(SNG2025010, 2023SS03, SYG2024144, SYG2025129)

详细信息

作者简介:
曹昌龙：男，硕士生，研究方向为多路径传输协议、强化学习

李领治：男，副教授，研究方向为智能物联网、多智能体协同

施连敏：男，高级工程师，研究方向为物联网与嵌入式人工智能

赵庆越：男，硕士生，研究方向为无人机路径规划

通讯作者:
李领治　lilingzhi@suda.edu.cn

中图分类号: TN915.04
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- 被引次数: 0
出版历程
- 收稿日期: 2026-01-04
- 修回日期: 2026-04-02
- 录用日期: 2026-04-09
- 网络出版日期: 2026-04-30

Multipath Scheduling Algorithm for UAV Video Streaming

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)

摘要

摘要: 在无人机实时视频流传输场景中，可利用多路径传输协议的带宽聚合优势提升视频体验质量(QoE)。针对该协议在无人机网络环境下面临的动态异构挑战，该文提出一种基于神经置信上界(NeuralUCB)算法的多路径调度框架NeuroFly。首先，将多路径流量调度建模为上下文多臂老虎机(CMAB)问题。然后，基于NeuralUCB设计在线学习策略：融合多维度异构特征构建上下文空间，引入帧优先级驱动的冗余传输机制构建动作空间，并结合多目标奖励函数设计，实现动态异构网络下的自适应流量调度。此外，还设计一种上下文监控机制，能够实时检测上下文分布变化并适时重启学习过程，以提升NeuroFly对环境突变的应对能力。最后，仿真和野外环境下的实验结果表明，与现有先进调度算法和传输方案相比，NeuroFly第99百分位延迟降低19.9%～51.0%，并在多项视频QoE指标上取得显著领先：平均视频帧率提升最高达24.6%，图像结构相似性提升最高达49.2%，缓冲时间占比减少13.4%～77.6%，证明其在无人机动态异构网络下具有更为出色的持续传输优化能力，能够有效降低传输延迟并提升视频QoE。
- 多路径传输协议 /
- 流量调度 /
- 上下文多臂老虎机 /
- 无人机网络 /
- 视频体验质量
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.
- Multipath transport protocol /
- Traffic scheduling /
- Contextual Multi-Armed Bandit (CMAB) /
- Unmanned Aerial Vehicle (UAV) networks /
- Video Quality of Experience (QoE)

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图 1 NeuroFly系统架构

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图 2 多变量漂移检测示意图

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图 3 网络拓扑

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图 4 仿真环境下的实时视频流传输评估

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图 5 实验硬件平台

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图 6 野外环境下的实时视频流传输评估

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1 NeuroFly多路径调度算法

输入：调度周期持续时间$ {T}_{\text{S}} $，冗余粒度参数$ K $
输出：冗余率决策
(1) 初始化：神经网络参数$ {\theta }_{0} $，经验回放池$ M $
(2) for 时间步$ t=1,2,\cdots,T $ do
(3) 　获取当前上下文观测$ \{{\boldsymbol{x}}_{t,{{\mathbf{a}}_{s}}}\}_{s=0}^{K} $
(4) 　for each$ a\in \mathcal{A}=\{{\mathbf{a}}_{0},{\mathbf{a}}_{1},\cdots ,{\mathbf{a}}_{K}\} $do
(5) 　　计算动作$ \boldsymbol{a} $的置信上界$ U_t^{\boldsymbol{a}} $
(6) 　　令动作$ \boldsymbol{a}_t=\mathrm{arg}\max_{\boldsymbol{a}\in\mathcal{A}}U_t^{\boldsymbol{a}} $
(7) 　end for
(8) 　执行动作$ \boldsymbol{a}_t $，开始冗余传输
(9) 　在调度周期结束时刻计算奖励$ {r}_{t} $
(10) 将样本$ \left\langle {\boldsymbol{x}}_{t,{{\mathbf{a}}_{t}}},{r}_{t}\right\rangle $存入经验回放池$ M $
(11) 从$ M $中随机采样一个批次的历史经验
(12) 通过随机梯度下降更新网络参数$ {\theta }_{t} $
(13) end for

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表 1 基础路径配置

	RTT(ms)	抖动(ms)	丢包率(%)	带宽(Mbit/(s·Hz))
Path 1	25～50	0～10	0～3	20～30
Path 2	50～100	0～20	0～3	20～30

下载: 导出CSV

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