The universal proliferation of mobiles devices, and specifically of smartphones with rich sensing capabilities, has given rise to a new fast-growing paradigm of sensing: mobile crowdsensing. Mobile crowdsensing (MCS) takes advantage of the ubiquity of the devices to process and collect information through voluntary sensing.

Blind Source Separation (BSS) deals with the recovery of source signals from a set of observed mixtures, when little or no knowledge of the mixing process is available. BSS can find an application in the context of network coding, where relaying linear combinations of packets maximizes the throughput and increases the loss immunity. By relieving the nodes from the need to send the combination coefficients, the overhead cost is largely reduced. However, the scaling ambiguity of the technique and the quasi-uniformity of compressed media sources makes it unfit, at its present state, for multimedia transmission. In order to open new practical applications for BSS in the context of multimedia transmission, we have recently proposed to use a non-linear encoding to increase the discriminating power of the classical entropy-based separation methods. Here, we propose to append to each source a non-linear message digest, which offers an overhead smaller than a per-symbol encoding and that can be more easily tuned. Our results prove that our algorithm is able to provide high decoding rates for different media types such as image, audio, and video, when the transmitted messages are less than 1.5 kilobytes, which is typically the case in a realistic transmission scenario.

In Blind Source Separation, or BSS, a set of source signals are recovered from a set of mixed observations without knowledge of the mixing parameters. Originated for real signals, BSS has recently been applied to finite fields, enabling more practical applications. However, classical entropy-based techniques do not perform well in finite fields. Here, we propose a non-linear encoding of the sources to increase the discriminating power of the separation methods. Our results show that the encoding improves the success rate of the separation for sources with few samples in large finite fields, both conditions met in practical networking applications. Our results open new possibilities in the context of network coding-wherein linear combinations of packets are sent in order to maximize throughput and increase loss immunity- by relieving the nodes from the need to send the combination coefficients, thus reducing the overhead cost.

Network coding (NC) can achieve the maximum information flow in the network by allowing nodes to combine received packets before retransmission. Several papers have shown NC to be beneficial in mobile ad-hoc networks, but the delay introduced by buffered decoding raises a problem in real-time streaming applications. Here we propose to use NC jointly with multiple description coding (MDC) to allow instant decoding of the received packets. The optimal encoding coefficients are chosen via distributed optimisation of the expected video quality. Nodes receive up-to-date information about the network topology through a recently proposed protocol, originally designed for real-time streaming of MDC video. Results show that, due to the limitations imposed by instant decoding to the coding window size, our approach consistently outperforms the popular technique of random linear network coding.

Multi-view video streaming is an emerging video paradigm that enables new interactive services, such as free viewpoint television and immersive teleconferencing. However, it comes with a high bandwidth cost, as the equivalent of many single-view streams has to be transmitted. Network coding (NC) can improve the performance of the network by allowing nodes to combine received packets before retransmission. Several works have shown NC to be beneficiai in wireless networks, but the delay introduced by buffering before decoding raises a problem in real-time streaming applications. Here, we propose to use Expanding Window NC (EWNC) for multi-view streaming to allow immediate decoding of the received packets. The order in which the packets are included in the coding window is chosen via RD-optimization for the current sending opportunity. Results show that our approach consistently outperforms both classical NC applied on each view independently and transmission without NC.

In this paper, we address the challenge of delivering a video stream, encoded with multiple descriptions, in a mobile ad-hoc environment with low-latency constraints. This kind of application is meant to provide an efficient and reliable video communication tool in scenarios where the deployment of an infrastructure is not feasible, such as military and disaster relief applications. First, we present a recently proposed protocol that employs a reliable form of one-hop broadcast to build an efficient overlay network according to a multi-objective function that minimizes the number of packets injected in the network and maximizes the path diversity among descriptions. Then, we introduce the main contribution of this paper: a cross-layer congestion control strategy where the MAC layer is video-coding aware and adjusts its transmission parameters (namely, the RTS retry limit) via congestion/distortion optimization. The main challenge in this approach is providing a reliable estimation of congestion and distortion, given the limited information available at each node. Our simulations show that, if a stringent constraint of low delay is imposed, our technique grants a consistent gain in terms of both PSNR and delay reduction, for bitrates up to a few megabits per second.

Multiple description coding (MDC) is a framework designed to improve the robustness of video content transmission in lossy environments. In this work, we propose an MDC technique using a legacy coder to produce two descriptions, based on separation of even and odd frames. If only one description is received, the missing frames are reconstructed using temporal high-order motion interpolation (HOMI), a technique originally proposed for distributed video coding. If both descriptions are received, the frames are reconstructed as a block-wise linear combination of the two descriptions, with the coefficient computed at the encoder in a RD-optimised fashion, encoded with a context-adaptive arithmetic coder, and sent as side information. We integrated the proposed technique in a mobile ad-hoc streaming protocol, and tested it using a group mobility model. The results show a non-negligible gain for the expected video quality, with respect to the reference technique.

Multiple description coding is a framework adapted to noisy transmission environments. In this work, we use H.264 to create two descriptions of a video sequence, each of them assuring a minimum quality level. If both of them are received, a suitable algorithm is used to produce an improved quality sequence. The key technique is a temporal image interpolation using motion compensation, inspired to the distributed video coding context. The interpolated image blocks are weighted with the received blocks obtained from the other description. The optimal weights are computed at the encoder and efficiently sent to the decoder as side information. The proposed technique shows a remarkable gain for central decoding with respect to similar methods available in the state of the art.