54 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
Vehicular Data Cloud Services
The advance cloud computing has provided an opportunity to resolve the challenges which effects by increasing transportation issues. Two methods of cloud services are available these are parking and mining. Mobile cloud computing has improved the storage capacity, stand by time of mobile terminals by migrating data processing to the remote cloud. The introduction of smart phones, cloud computing the automotive system is shifting toward the internet of vehicles
Exploiting Non-Causal CPU-State Information for Energy-Efficient Mobile Cooperative Computing
Scavenging the idling computation resources at the enormous number of mobile
devices can provide a powerful platform for local mobile cloud computing. The
vision can be realized by peer-to-peer cooperative computing between edge
devices, referred to as co-computing. This paper considers a co-computing
system where a user offloads computation of input-data to a helper. The helper
controls the offloading process for the objective of minimizing the user's
energy consumption based on a predicted helper's CPU-idling profile that
specifies the amount of available computation resource for co-computing.
Consider the scenario that the user has one-shot input-data arrival and the
helper buffers offloaded bits. The problem for energy-efficient co-computing is
formulated as two sub-problems: the slave problem corresponding to adaptive
offloading and the master one to data partitioning. Given a fixed offloaded
data size, the adaptive offloading aims at minimizing the energy consumption
for offloading by controlling the offloading rate under the deadline and buffer
constraints. By deriving the necessary and sufficient conditions for the
optimal solution, we characterize the structure of the optimal policies and
propose algorithms for computing the policies. Furthermore, we show that the
problem of optimal data partitioning for offloading and local computing at the
user is convex, admitting a simple solution using the sub-gradient method.
Last, the developed design approach for co-computing is extended to the
scenario of bursty data arrivals at the user accounting for data causality
constraints. Simulation results verify the effectiveness of the proposed
algorithms.Comment: Submitted to possible journa
TeamUp5G: a multidisciplinary approach to training and research on new RAN techniques for 5G ultra-dense mobile networks
Proceeding of: 12th IEEE/IET International Symposium on Communication Systems, Networks and Digital Signal Processing, (CSNDSP), 20-22, July 2020, (online).This paper presents a summary of the main research directions being followed in TeamUp5G European Training Network, teaming up a new generation of researchers and entrepreneurs ready to address complex engineering problems and innovation to work both at university and industry in the 5G field. This project is focused on new radio access network (RAN) techniques for 5G, considering ultradense mobile networks as a key ingredient of the actual mobile networks and their evolution. Research covers a wide spread of topics from physical layer and medium access control to applications, looking at spectrum sharing and energy efficiency as important features.This work has received funding from the European Union (EU) Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie ETN TeamUp5G, grant agreement No. 813391
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