Constructing a joint representation invariant across different modalities (e.g., video, language) is of significant importance in many multimedia applications. While there are a number of recent successes in developing effective image-text retrieval methods by learningjoint representations, the video-text retrieval task, in contrast, has not been explored to its fullest extent. In this paper, we study howto effectively utilize available multi-modal cues from videos for the cross-modal video-text retrieval task. Based on our analysis,we propose a novel framework that simultaneously utilizes multimodal features (different visual characteristics, audio inputs, and text) by a fusion strategy for efficient retrieval. Furthermore, we explore several loss functions in training the joint embedding and propose a modified pairwise ranking loss for the retrieval task. Experiments on MSVD and MSR-VTT datasets demonstrate that our method achieves significant performance gain compared to the state-of-the-art approaches.