Designing QoE Experiments to Evaluate PeertoPeer Streaming Applications Quality of Experience (QoE) refers to subjective criteria for evaluating multimedia content. Methods have been devised to study the design of Voice over IP systems and video codecs. In recent years, due to more abundant network bandwidth, it has become quite popular to watch video streamed over the Internet, whether by clientserver method or through a peertopeer (P2P) network. In this paper, we describe our experience in conducting QoE studies of P2P streaming using a chunklevel model. Instead of considering finegrained network service impairments such as bit errors, packet losses or delays, we focus on chunk level delays. We carry out some preliminary QoE experiments on lowbit rate, lowframe rate and lowresolution video (3Lvideo) sequences. We apply the chunklevel model to help improve the design of the P2P streaming algorithms, and the design of video players that playback network streamed video.

Improving the QoE of OTT Multimedia Services in Wireless Scenarios One of the goals of an operator is to improve the Quality of Experience (QoE) of a client in networks where Overthetop (OTT) content is being delivered, as the Internet has now become the xe2x80x98de factoxe2x80x98 path for video transmission, with video data representing over 75% of the entire network traffic, and the usersxe2x80x99 desires to consume more content continuing to show exponential growth. This paper proposes a new approach to improve the clientsxe2x80x99 QoE by using network awareness models as a way to forecast the number of future video content chunks to be cached. Once cached, these chunks become closer to the client, which mitigates the consequences of suboptimal network performance on bitrateadaptive HTTP video streaming. Two prefetching approaches are proposed: an Analytic Hierarchy Process (AHP) through a weighted function on the measured quality of service metrics, and a Neural Network to estimate and maximize the user experience without the normal service delivery degradation. Our evaluation shows that AHP provides a suboptimal behavior under constrained bandwidth, while a neural network model is able to infer network behavior and predict an optimized MOS, allowing it to better optimize the caching system and the required prefetching to adequately improve QoE.