With exponentially growing quantity of video content in various formats, including the popularisation of HD (High Definition) video and cinematographic content, the problem of efficient indexing and retrieval in video databases becomes crucial. Despite efficient methods have been designed for the frame-based queries on video with local features, object-based indexing and retrieval attract attention of research community by the seducing possibility to formulate meaningful queries on semantic objects. In the case of HD video, the principle of scalability addressed by actual compression standards is of great importance. It allows for indexing and retrieval on the lower resolution available in the compressed bit-stream. The wavelet decomposition used in the JPEG2000 standard provides this property. In this paper, we propose a scalable indexing of video content by objects. First, a method for scalable moving object extraction is designed. Using the wavelet data, it relies on the combination of robust global motion estimation with morphological colour segmentation at a low spatial resolution. It is then refined using the scalable order of data. Second, a descriptor is built only on the objects extracted. This descriptor is based on multi-scale histograms of wavelet coefficients of objects. Comparison with SIFT features extracted on segmented object masks gives promising results.