This paper describes a statistical integration algorithm for color, motion and stereo disparity, and introduces a real-time stereo system that can tell us where and what objects are moving. Regarding the integration algorithm, motion estimation and depth estimation are simultaneously performed by a clustering process based on motion, stereo disparity, color, and pixel position. As a result of the clustering, an image is decomposed into region fragments. Eath fragment is characterized by distribution parameters of spatiotemporal intensity gradients, stereo difference, color and pixel positions. Motion vectors and stereo disparities for each fragment are obtained from those distribution parameters. The real-time stereo system can view the objects with the distribution parameters over frames. The implementation and experiments show that we can utilize the proposed algorithm in real-time applications such as surveillance and human-computer interaction.

In this paper, we propose an RTP/UDP/IP header compression method, Multiple-Reference Compression (MRC), which is designed for mobile multimedia communications. MRC is a compression method that calculates differences from the multiple reference headers that have already been sent and inserts them into a compressed header. The receiver can decompress the compressed header as long as at least one of the reference headers is correctly received and decompressed. MRC improves robustness against packet losses compared with CRTP defined in IETF RFC2508, and imposes less overheads and computational burden than robust header compression (ROHC) defined in RFC3095. We also implemented MRC and other header compression algorithms into our mobile testbed, and conducted multimedia streaming experiments over the testbed. The results of the experiments show that MRC offers the same level of packet loss rate as Legacy RTP for both audio and video streams, and provides better media quality than Legacy RTP and CRTP on error-prone radio links. Header compression robust against packet losses is expected as a key technology for VoIP and multimedia streaming services over 3G and future mobile networks.

This paper presents an algorithmic approach to acquiring the influencing relationships among users by discovering implicit influencing group structure from smartphone usage. The method assumes that a time series of users' application downloads and activations can be represented by individual inter-personal influence factors. To achieve better predictive performance and also to avoid over-fitting, a latent feature model is employed. The method tries to extract the latent structures by monitoring cross validating predictive performances on approximated influence matrices with reduced ranks, which are generated based on an initial influence matrix obtained from a training set. The method adopts Nonnegative Matrix Factorization (NMF) to reduce the influence matrix dimension and thus to extract the latent features. To validate and demonstrate its ability, about 160 university students voluntarily participated in a mobile application usage monitoring experiment. An empirical study on real collected data reveals that the influencing structure consisted of six influencing groups with two types of mutual influence, i.e. intra-group influence and inter-group influence. The results also highlight the importance of sparseness control on NMF for discovering latent influencing groups. The obtained influencing structure provides better predictive performance than state-of-the-art collaborative filtering methods as well as conventional methods such as user-based collaborative filtering techniques and simple popularity.

In this paper, we present a CDN (Content Delivery Network) architecture for mobile streaming service in which content segmentation, request routing, pre-fetch scheduling, and session handoff are controlled by SMIL (Synchronized Multimedia Integration Language) modification. In this architecture, mobile clients simply follow modified SMIL files downloaded from a portal server; these modifications enable multimedia content to be delivered to the mobile clients from the best surrogates in the CDN. The key components of this architecture are 1) content segmentation with SMIL modification, 2) on-demand rewriting of URLs in SMIL, 3) pre-fetch scheduling based on timing information derived from SMIL, and 4) SMIL updates by SOAP (Simple Object Access Protocol) messaging for session handoffs due to client mobility. This architecture enhances streaming media quality for mobile clients while utilizing network resources efficiently and supporting client mobility in an integrated and practical way. The current status of our prototype on a mobile QoS testbed "MOBIQ" is also reported in this paper.