The current Cellular Vehicle-to-Everything (C-V2X) Sidelink communication protocol provides a low latency interface for sharing short safety messages among Road-Side Units and vehicles. However, while its packets are broadcasted in the channel, the throughput is vulnerable to channel conditions and cannot meet the needs of the emerging connected and autonomous vehicles applications (e.g. vehicular fusion tasks), which require multimodal and multi-source sensor data sharing. In this work, we establish a C-V2X testbed on the campus of the University of California, San Diego, to study the feasibility of using C-V2X Sidelink communications for transmitting sensor data in real-time. We implement an end-to-end RGB sensor data (i.e. camera image frames) transmission mechanism on the C-V2X Sidelink testbed and explore the corresponding Quality of Service (QoS) characteristics under two configurable link-level parameters, the Modulation and Coding Scheme (MCS) and packet size. We then propose a cross-layer predictive and adaptive framework which adjusts, in real-time, the MCS and packet size settings based on side-channel information to optimize the QoS for image frame transmission. The real-world trace-driven emulation shows that the proposed policy improves the average frame goodput performance by 28% compared to fixed configuration policies that are used in current Sidelink communications.