In this paper, we propose a novel dataset called the Quality-Oriented Database for Video Capsule Endoscopy (QVCED) which serves as the primary crucial resource for evaluating the quality of Wireless Capsule Endoscopy (WCE) images and videos. Serving as a benchmark, the QVCED encourages the design of learning-based enhancement methods to address image quality assessment and enhancement challenges in WCE. This comprehensive dataset consists of a large number of WCE videos encompassing common distortions encountered in clinical practice, including noise, defocus blur, motion blur, and uneven illumination. Moreover, video quality has been intentionally degraded at varying distortion severity levels to faithfully replicate real-world conditions. The extensive analysis demonstrates the diversity and practical relevance of this dataset in the WCE domain that motivates the advancement of a more precise diagnosis regarding gastrointestinal disorders. The complete dataset is publicly available through the following link: https://cloud.math.univ-paris13.fr/index.php/s/b74TQk7mMpHDXKT.