Recent advances in generative text-to-video AI models (e.g., VideoPoet and Sora) have spurred a surge in video production, leading to an increased demand for video processing pipelines among various video service providers such as YouTube and TikTok. With the improvement of cloud computing, video processing systems are frequently updated and present both opportunities and challenges while optimizing the quality of service (QoS) and cloud resource utilization. However, research on evaluating the performance of video processing systems is limited. Besides the availability of video datasets and realistic workloads, the lack of an open-source benchmark system reflecting the characteristics of industrial video processing systems is a significant gap. To fill this gap, we develop IrisBench, an open-source benchmark suite for cloud video processing systems to facilitate research on performance analysis. Our benchmark systems include three video services: video transcoding, video partitioning, and video object detection services. Our future work relies on using IrisBench to study the architectural implications of various cloud video processing systems in the cloud.