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We present in this paper a novel approach to improve TCP performance in the mobile wireless networks (MANET). The proposed protocol, designated as HYBRID TCP, is essentially an extension of the original IEEE 802.11 standard and the TCP-Reno protocol. By making some modifications using a cross layer solution to the legacy IEEE 802.11 MAC and TCP-Reno protocol, HYBRID TCP provides a significant improvement in the performance of TCP in multi-hop wireless environments by taking in consideration the mobility of nodes, the interferences and solves the problem of multi-hops. The performance of HYBRID TCP is compared to that of standard TCP-Reno scheme and to a recent improvement of TCP performed on the basis of the signal strength in terms of achieved effective throughput and of transmission time. The numerical results reveal that HYBRID TCP achieves a significant improvement in the TCP transmission performance over mobile multi-hop wireless networks.

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The packet scheduling in router plays an important role in the sense to achieve QoS differentiation and to optimize the queuing delay, in particular when this optimization is accomplished on all routers of a path between source and destination. In a dynamically changing environment a good scheduling discipline should be also adaptive to the new traffic conditions. To solve this problem we use a multi-agent system in which each agent tries to optimize its own behaviour and communicate with other agents to make global coordination possible. This communication is done by mobile agents. In this paper, we adopt the framework of Markov decision processes applied to multi-agent system and present a pheromone-Q learning approach which combines the standard Q-learning technique with a synthetic pheromone that acts as a communication medium speeding up the learning process of cooperating agents.

The packet scheduling in router plays an important role in the sense to achieve QoS differentiation and to optimize the queuing delay, in particular when this optimization is accomplished on all routers of a path between source and destination. In a dynamically changing environment a good scheduling discipline should be also adaptive to the new traffic conditions. We model this problem as a multiagent system in which each agent learns through continual interaction with the environment in order to optimize its own behaviour. So, we adopt the framework of Markov decision processes applied to multi-agent system and present a pheromone-Q learning approach which combines the Qmulti-learning technique with a synthetic pheromone that acts as a communication medium speeding up the learning process of cooperating agents.

Nowadays, Media streaming services over TCP have become very popular because of the TCP's reliability, which provides remarkable stability to the Internet. However, in order to offer a high media quality and a good user satisfaction, the media streaming service requires that transport protocols can be adapted continuously with the network parameters. However, the diversity, of terminals (i.e., tablet, smart phones, laptop … etc.) and their corresponding capabilities, means that users' agnostic solutions are inefficient to cope with such diverse contexts. Indeed, the intrinsic characteristics and parameters of the terminal users (i.e., devices) need to be taken into account on the video streaming adaptation process. The classic adaptive video streaming services do not consider the user parameters on the adaptation process. In this paper, we propose an adaptive video streaming solution to improve the user satisfaction factor by adapting the TCP parameters according to the user's parameters on mobile networks. The user satisfaction factor is calculated according to some metrics driven from the user's quality of experience (QoE). The work is validated through our proposal based on a new mobile agent (which does all the work) developed on a Linux script platform and tested on different kinds of devices with different scenarios.

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