Abstract
The recent advancements of wireless technology and applications make downlink scheduling and resource allocations an important research topic. In this paper, we consider the problem of downlink scheduling for multi-user scalable video streaming over orthogonal frequency division multiple access (OFDMA) channels. The video streams are precoded using a scalable video coding (SVC) scheme. We propose a fuzzy logic-based scheduling algorithm, which prioritises the transmission to different users by considering video content, and channel conditions. Furthermore, a novel analytical model and a new performance metric have been developed for the performance analysis of the proposed scheduling algorithm. The obtained results show that the proposed algorithm outperforms the content-blind/channel aware scheduling algorithms with a gain of as much as 19% in terms of the number of supported users. The proposed algorithm allows for a fairer allocation of resources among users across the entire sector coverage, allowing for the enhancement of video quality at edges of the cell while minimising the degradation of users closer to the base station.
Highlights
Supporting multimedia applications and services over wireless networks is challenging due to constraints and heterogeneities such as limited bandwidth, limited battery power, random time-varying channel conditions, different protocols and standards, and varying quality of service (QoS) requirements
Utilising fuzzy inference system (FIS), we propose a downlink scheduling algorithm and a user utility function, which complements our study
It is important to note that content-blind schedulers do not produce accurate results as video contents contain different spatio-temporal features, which are their unique signatures, which this paper aims to address by proposing an intelligent fuzzy logic-based content-aware and channel-aware downlink scheduling algorithm for scalable videos over long-term evolution (LTE) Networks
Summary
Supporting multimedia applications and services over wireless networks is challenging due to constraints and heterogeneities such as limited bandwidth, limited battery power, random time-varying channel conditions, different protocols and standards, and varying quality of service (QoS) requirements. It is worth mentioning that channel-unaware schedulers make no use of channel state conditions such as power level, channel error and loss rates. These basically focus on fulfilling delay and throughput constraints. Examples of the traditional channel-unaware schedulers are Round-Robin, weighted fair queuing (WFQ) and priority-based algorithms. Such algorithms assume perfect channel conditions, no loss and unlimited power source. After the input and output variables are defined for the Fuzzy system, the step is to assign linguistic labels in order to provide quantification of the values, which are defined through membership functions. More details on the functionality of FIS can be found in [20,21]
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