Mobile Ad Hoc Networks (MANETs) have been an important research topic for the last years, playing a crucial role within the fast growing sector of mobile communications. At the same time, video applications over mobile devices are becoming widely used by nowadays mobile clients, where the quality in the transmission of such contents will determine the success of these applications in the future. Therefore, it is mandatory to find the best strategies to guarantee a good Quality of Service (QoS) to the end-user. In this work we present a set of novel strategies to improve the performance of video transmission over MANETs. These new strategies are based on distributed admission control protocols which has proved to be helpful at achieving an efficient video transmission system. Experimental results show that, when adopting the new strategies to determine the optimal number of layers to transmit, we can achieve better results compared to other existent approaches in terms of idle time periods, fairness and delay.

Streaming applications will rapidly develop and contribute a significant amount of traffic in the near future. A problem, scarcely addressed so far, is how to distribute video streaming traffic from one source to all nodes in an urban vehicular network. This problem significantly differs from previous work on broadcast and multicast in ad hoc networks because of the highly dynamic topology of vehicular networks and the strict delay requirements of streaming applications. We present a solution for intervehicular communications, called Streaming Urban Video (SUV), that 1) is fully distributed and dynamically adapts to topology changes, and 2) leverages the characteristics of streaming applications to yield a highly efficient, cross-layer solution.

Emerging multimedia applications over mobile devices are becoming very popular, especially over infrastructure wireless networks such as cellular and WLANs. However, providing this kind of services over infrastructure-less networks like ad hoc networks presents many additional problems. One of these problems is how to share resources fairly among the users involved. In this article we propose a QoS framework supporting scalable video streaming in mobile ad hoc networks based on distributed admission control and video traffic awareness. Our framework promotes fairness between video flows in terms of resource consumption. It also guarantees a significant reduction of the idle times experienced by users during periods of network saturation, thus increasing the video playout time in reception for all users. Using the IEEE 802.11e MAC technology as our basis for traffic differentiation, our framework, called DACME-SV (Distributed Admission Control for MANET's - Scalable Video), relies on a periodic probing process to measure the available bandwidth and the end-to-end delay on the path. DACME-SV adopts a cross-layer approach to determine the optimum number of video layers to transmit at any given time, thus avoiding network congestion and guaranteeing an acceptable video quality at the destination. Experimental results show that idle time periods are substantially decreased, while exhibiting a good overall performance in terms of throughput and delay.

Streaming applications will rapidly develop and contribute a significant amount of traffic in the near future. A problem, scarcely addressed so far, is how to support streaming traffic in vehicular networks (VANETs). This problem significantly differs from previous work on broadcast and multicast in ad hoc networks, because of the highly-dynamic topology of VANETs and the strict delay requirements of streaming applications. Our solution, completely relies on inter-vehicular communication for the distribution of multimedia content in a urban environment and it has the following appealing features: (i) it is fully distributed and dynamically adapts to topology changes; (ii) it leverages the characteristics of streaming applications to yield a highly-efficient, cross-layer solution. After optimally dimensioning the network system, we compare the performance of our solution against theoretical results for broadcast capacity in multihop networks.