Vehicle-to-infrastructure collaborative perception (V2I-CP) leverages a high-vantage node to transmit supplementary information, i.e., bird's-eye-view (BEV) feature maps, to vehicles, effectively overcoming line-of-sight limitations. However, the downlink V2I transmission introduces a significant communication bottleneck. Moreover, vehicles in V2I-CP require \textit{heterogeneous yet overlapping} information tailored to their unique occlusions and locations, rendering standard unicast/broadcast protocols inefficient. To address this limitation, we propose \textit{Birdcast}, a novel multicasting framework for V2I-CP. By accounting for individual maps of interest, we formulate a joint feature selection and multicast grouping problem to maximize network-wide utility under communication constraints. Since this formulation is a mixed-integer nonlinear program and is NP-hard, we develop an accelerated greedy algorithm with a theoretical $(1 - 1/\sqrt{e})$ approximation guarantee. While motivated by CP, Birdcast provides a general framework applicable to a wide range of multicasting systems where users possess heterogeneous interests and varying channel conditions. Extensive simulations on the V2X-Sim dataset demonstrate that Birdcast significantly outperforms state-of-the-art baselines in both system utility and perception quality, achieving up to 27\% improvement in total utility and a 3.2\% increase in mean average precision (mAP).

翻译：摘要：车-基础设施协同感知（V2I-CP）利用高视角节点向车辆传输补充信息（即鸟瞰图特征图），有效克服了视线受限问题。然而，下行V2I传输带来了显著的通信瓶颈。此外，V2I-CP中的车辆需要根据其独特的遮挡情况和位置获取\textit{异构但重叠}的信息，这使得标准的单播/广播协议效率低下。针对这一局限，我们提出\textit{Birdcast}——一种面向V2I-CP的新型多播框架。通过考虑各车辆的个性化兴趣图，我们构建了一个联合特征选择与多播分组问题，以在通信约束下最大化网络整体效用。由于该问题属于混合整数非线性规划且为NP难问题，我们开发了一种加速贪婪算法，并给出了$(1 - 1/\sqrt{e})$的理论近似保证。尽管Birdcast源于协同感知场景，但其提供了适用于多种多播系统的通用框架——在这些系统中，用户具有异构兴趣和变化的信道条件。在V2X-Sim数据集上的大量实验表明，Birdcast在系统效用和感知质量上均显著优于现有基线方法，总效用提升高达27%，平均精度均值（mAP）提升3.2%。