Adaptive end-to-end optimization of mobile video streaming using QoS negotiation

Video streaming over wireless links is a non-trivial problem due to the large and frequent changes in the quality of the underlying radio channel combined with latency constraints. We believe that every layer in a mobile system must be prepared to adapt its behavior to its environment. Thus layers must be capable of operating in multiple modes; each mode will show a different quality and resource usage. Selecting the right mode of operation requires exchange of information between interacting layers. For example, selecting the best channel coding requires information about the quality of the channel (capacity, bit-error-rate) as well as the requirements (latency, reliability) of the compressed video stream generated by the source encoder. In this paper we study the application of our generic QoS negotiation scheme to a specific configuration for mobile video transmission. We describe the results of experiments studying the overall effectiveness, stability, and dynamics of adaptation of our distributed optimization approach

Distributed Content Based Video Identification in Peer-to-Peer Networks: Requirements and Solutions

In this paper, we first discuss the essential requirements for a fingerprint (perceptual hash)-based distributed video identification system in peer-to-peer (P2P) networks in comparison with traditional central database implementations of fingerprints. This discussion reveals that first, fingerprint sizes of existing video fingerprint methods are not compatible with the cache sizes of current P2P clients; second, fingerprint extraction durations during a query are not at tolerable levels for a user in the network; third, the repetitive patterns in the extracted fingerprints avoid the uniform distribution of storage and traffic load among the peers; and finally, the existing methods do not provide a solution to synchronize the fingerprint extraction from the shared video and queried video. In order to solve the mentioned requirements, we propose a baseline method using only the difference of video framemeans, which decreases the fingerprint sizes to typical cache sizes, by increasing the granularity levels from seconds to minutes. We then develop a novel algorithm which utilizes reference points on one-dimensional frame mean sequence for the synchronization of fingerprint extraction. This algorithm is extended with a hierarchical decoding approach based on Gaussian scales, which only decodes a subset of video frames without needing a full decoding. Finally, an analysis on the effect of design parameters to the fingerprint probability distribution is performed to avoid repetitive patterns. Our ultimate solution reduces the fingerprint sizes into kilobytes, extraction time to seconds, and search duration into milliseconds, and achieves about 90% detection rates with 1-4 min granularities, while enabling a fair distribution of storage load among the peers at the same time

Encrypted signal processing for privacy protection

In recent years, signal processing applications that deal with user-related data have aroused privacy concerns. For instance, face recognition and personalized recommendations rely on privacy-sensitive information that can be abused if the signal processing is executed on remote servers or in the cloud. In this tutorial article, we introduce the fusion of signal processing and cryptography as an emerging paradigm to protect the privacy of users. While service providers cannot access directly the content of the encrypted signals, the data can still be processed in encrypted form to perform the required signal processing task. The solutions for processing encrypted data are designed using cryptographic primitives like homomorphic cryptosystems and secure multiparty computation (MPC)

Music2Share - Copyright-Compliant Music Sharing in P2P Systems (Invited paper)

Peer-to-Peer (P2P) networks are generally considered to be free havens for pirated content, in particular with respect to music. We describe a solution for the problem of copyright infringement in P2P networks for music sharing. In particular, we propose a P2P protocol that integrates the functions of identification, tracking and sharing of music with those of licensing, monitoring and payment. This highly decentralized music-aware P2P protocol will allow access to large amounts of music of guaranteed quality; it merges in a natural way the policing functions for copyright protection and an efficient music-management infrastructure for the benefit of the user