2,826 research outputs found
Flow Level QoE of Video Streaming in Wireless Networks
The Quality of Experience (QoE) of streaming service is often degraded by
frequent playback interruptions. To mitigate the interruptions, the media
player prefetches streaming contents before starting playback, at a cost of
delay. We study the QoE of streaming from the perspective of flow dynamics.
First, a framework is developed for QoE when streaming users join the network
randomly and leave after downloading completion. We compute the distribution of
prefetching delay using partial differential equations (PDEs), and the
probability generating function of playout buffer starvations using ordinary
differential equations (ODEs) for CBR streaming. Second, we extend our
framework to characterize the throughput variation caused by opportunistic
scheduling at the base station, and the playback variation of VBR streaming.
Our study reveals that the flow dynamics is the fundamental reason of playback
starvation. The QoE of streaming service is dominated by the first moments such
as the average throughput of opportunistic scheduling and the mean playback
rate. While the variances of throughput and playback rate have very limited
impact on starvation behavior.Comment: 14 page
360° mulsemedia experience over next generation wireless networks - a reinforcement learning approach
The next generation of wireless networks targets aspiring key performance indicators, like very low latency, higher data rates and more capacity, paving the way for new generations of video streaming technologies, such as 360° or omnidirectional videos. One possible application that could revolutionize the streaming technology is the 360° MULtiple SEnsorial MEDIA (MULSEMEDIA) which enriches the 360° video content with other media objects like olfactory, haptic or even thermoceptic ones. However, the adoption of the 360° Mulsemedia applications might be hindered by the strict Quality of Service (QoS) requirements, like very large bandwidth and low latency for fast responsiveness to the users, inputs that could impact their Quality of Experience (QoE). To this extent, this paper introduces the new concept of 360° Mulsemedia as well as it proposes the use of Reinforcement Learning to enable QoS provisioning over the next generation wireless networks that influences the QoE of the end-users
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