Toufik Ahmed

Badreddine Yacine Yacheur, Toufik Ahmed, Mohamed Mosbah

Cooperative intelligent transport systems rely on a set of Vehicle-to-Everything (V2X) applications to enhance road safety. Emerging new V2X applications like Advanced Driver Assistance Systems (ADASs) and Connected Autonomous Driving (CAD) applications depend on a significant amount of shared data and require high reliability, low end-to-end (E2E) latency, and high throughput. However, present V2X communication technologies such as ITS-G5 and C-V2X (Cellular V2X) cannot satisfy these requirements alone. In this paper, we propose an intelligent, scalable hybrid vehicular communication architecture that leverages the performance of multiple Radio Access Technologies (RATs) to meet the needs of these applications. Then, we propose a communication mode selection algorithm based on Deep Reinforcement Learning (DRL) to maximize the network's reliability while limiting resource consumption. Finally, we assess our work using the platooning scenario that requires high reliability. Numerical results reveal that the hybrid vehicular communication architecture has the potential to enhance the packet reception rate (PRR) by up to 30% compared to both the static RAT selection strategy and the multi-criteria decision-making (MCDM) selection algorithm. Additionally, it improves the efficiency of the redundant communication mode by 20% regarding resource consumption

Abbas Bradai, Toufik Ahmed, Samir Medjiah

Scalable video coding has drawn great interest in content delivery in many multimedia services thanks to its capability to handle terminal heterogeneity and network conditions variation. In our previous work, and under the umbrella of ENVISION, we have proposed a playout smoothing mechanism to ensure the uniform delivery of the layered stream, by reducing the quality changes that the stream undergoes when adapting to changing network conditions. In this paper we study the resulting video quality, from the final user perception under different network conditions of loss and delays. For that we have adopted the Double Stimulus Impairment Scale (DSIS) method. The results show that the Mean Opinion Score for the smoothed video clips was higher under different network configuration. This confirms the effectiveness of the proposed smoothing mechanism.

Abbas Bradai, Toufik Ahmed

There is an increasing demand for P2P streaming in particular for layered video. In this category of applications, the stream is composed of hierarchically encoded sub-streams layers namely the base layer and enhancements layers. We consider a scenario where the receiver peer uses the pull-based approach to adjust the video quality level to their capability by subscribing to different number of layers. We note that higher layers received without their corresponding lower layers are considered as useless and cannot be played, consequently the throughput of the system will drastically degrade. To avoid this situation, we propose an economical model based on auction mechanisms to optimize the allocation of sender peers' upload bandwidth. The upstream peers organize auctions to "sell" theirs items (links' bandwidth) according to bids submitted by the downstream peers taking into consideration the peers priorities and the requested layers importance. The ultimate goal is to satisfy the quality level requirement for each peer, while reducing the overall streaming cost. Through theoretical study and performance evaluation we show the effectiveness of our model in terms of users and network's utility.