The popularity of multimodal dialogue has stimulated the need for a new generation of dialogue agents with multimodal interactivity. When users communicate with customer service, they may express their requirements by means of text, images, or even videos. Visual information usually acts as discriminators for product models, or indicators of product failures, which play an important role in the E-commerce scenario. On the other hand, detailed information provided by the images is limited, and typically, customer service systems cannot understand the intent of users without the input text. Thus, bridging the gap between the image and text is crucial for communicating with customers. In this paper, we construct JDDC 2.1, a large-scale multimodal multi-turn dialogue dataset collected from a mainstream Chinese E-commerce platform, containing about 246K dialogue sessions, 3M utterances, and 507K images, along with product knowledge bases and image category annotations. Over our dataset, we jointly define four tasks: the multimodal dialogue response generation task,the multimodal query rewriting task, the multimodal dialogue discourse parsing task, and the multimodal dialogue summarization task.JDDC 2.1 is the first corpus with annotations for all the above tasks over the same dialogue sessions, which facilitates the comprehensive research around the dialogue. In addition, we present several text-only and multimodal baselines and show the importance of visual information for these tasks. Our dataset and implements will be publicly available.