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Enhancing V2V Communication by Parsimoniously Leveraging V2N2V Path in Connected Vehicles
Songmu Heo1, Yoo-Seung Song2, Seungmo Kang1
1Department of Computer Science and Engineering, College of Informatics, Korea University, Anam-Dong, Sungbuk-gu, Seoul 02841, Republic of Korea.
None:
The rapid proliferation of connected vehicles equipped with both Vehicle-to-Vehicle (V2V) sidelink and cellular interfaces creates new opportunities for real-time vehicular applications, yet achieving ultra-reliable communication without prohibitive cellular costs remains challenging. This paper addresses reliable inter-vehicle video streaming for safety-critical applications such as See-Through for Passing and Obstructed View Assist, which require stringent Service Level Objectives (SLOs) of 50 ms latency with 99% reliability. Through measurements in Seoul urban environments, we characterize the complementary nature of V2V and Vehicle-to-Network-to-Vehicle (V2N2V) paths: V2V provides ultra-low latency (mean 2.99 ms) but imperfect reliability (95.77%), while V2N2V achieves perfect reliability but exhibits high latency variability (P99: 120.33 ms in centralized routing) that violates target SLOs. We propose a hybrid framework that exploits V2V as the primary path while selectively retransmitting only lost packets via V2N2V. The key innovation is a dual loss detection mechanism combining gap-based and timeout-based triggers leveraging Real-Time Protocol (RTP) headers for both immediate response and comprehensive coverage. Trace-driven simulation demonstrates that the proposed framework achieves a 99.96% packet reception rate and 99.71% frame playback ratio, approaching lossless transmission while maintaining cellular utilization at only 5.54%, which is merely 0.84 percentage points above the V2V loss rate. This represents a 7× cost reduction versus PLR Switching (4.2 GB vs. 28 GB monthly) while reducing video stalls by 10×. These results demonstrate that packet-level selective redundancy enables cost-effective ultra-reliable V2X communication at scale.
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