LTE evolved Multimedia Broadcast/Multicast Service (eMBMS) is an attractive solution for video delivery to very large groups in crowded venues. However, the deployment and management of eMBMS systems are challenging, due to the lack of real-time feedback from the user equipments (TIEs). Therefore, we present the Dynamic Monitoring (DyMo) system for low-overhead feedback collection. DyMo leverages eMBMS for broadcasting stochastic group instructions to all UEs. These instructions indicate the reporting rates as a function of the observed quality of service (QoS). This simple feedback mechanism collects very limited QoS reports from the TIEs. The reports are used for network optimization, thereby ensuring high QoS to the TIEs. We present the design aspects of DyMo and evaluate its performance analytically and via extensive simulations. Specifically, we show that DyMo infers the optimal eMBMS settings with extremely low overhead while meeting strict QoS requirements under different TIE mobility patterns and presence of network component failures. For instance, DyMo can detect the eMBMS signal-to-noise ratio experienced by the 0.1th percentile of the TIEs with a root mean square error of 0.05% with only 5 to 10 reports per second regardless of the number of TIEs.

LTE evolved Multimedia Broadcast/Multicast Service (eMBMS) is an attractive solution for video delivery to very large groups in crowded venues. However, deployment and management of eMBMS systems is challenging, due to the lack of realtime feedback from the User Equipment (UEs). Therefore, we present the Dynamic Monitoring (DyMo) system for low-overhead feedback collection. DyMo leverages eMBMS for broadcasting Stochastic Group Instructions to all UEs. These instructions indicate the reporting rates as a function of the observed Quality of Service (QoS). This simple feedback mechanism collects very limited QoS reports from the UEs. The reports are used for network optimization, thereby ensuring high QoS to the UEs. We present the design aspects of DyMo and evaluate its performance analytically and via extensive simulations. Specifically, we show that DyMo infers the optimal eMBMS settings with extremely low overhead, while meeting strict QoS requirements under different UE mobility patterns and presence of network component failures. For instance, DyMo can detect the eMBMS Signal-to-Noise Ratio (SNR) experienced by the 0.1% percentile of the UEs with Root Mean Square Error (RMSE) of 0.05% with only 5 to 10 reports per second regardless of the number of UEs.

Video transmission is becoming an increasingly essential part of the current Internet; however before the high video quality at the client side becomes a reality, the best effort model has to adapt the video characteristic to the network conditions. In this paper, we are proposing a media middleware component that can be added to the edge routers in DiffServ network in order to have the responsibility to enhance QoS at the receivers' side. Passing such video frames to differentiated service network increases the video quality and treats the video frames based on their priorities. The media aware sub-layer has the responsibility to cache high priority video frames. We demonstrate through network simulation that the proposed technique decreases the average packet delay, and the packet loss, which yields a better video quality at the clients compared to existing techniques.

In this paper, we address the problem of collaborative video caching in ad hoc mobile networks. We consider network portraying static video server with wired interface to gateway node that is equipped with wireless interfaces, other nodes are requiring access to the video streams that is stored at video server. In order to reduce the average access latency as well as enhance the video accessibility, efficient video caching placement and replacement strategies are crucial at some of the distributed intermediate nodes across the network. Virtual backbone caching nodes will be elected by executing caching placement algorithm after running the routing protocol phase. The simulation results indicate that the proposed collaborative aggregate cache mechanism can significantly improve the video QoS in terms of packet loss and average packet delay.