QoE‐Aware Scheduling Algorithm for Adaptive HTTP Video Delivery in Wireless Networks

Abstract

In the last years, the video content consumed by mobile users has increased exponentially. Since mobile network capacity cannot be increased as fast as required, it is crucial to develop intelligent schedulers that allocate radio resources very efficiently and are able to provide a high Quality of Experience (QoE) to most of the users. This paper proposes a new and effective scheduling solution—the Maximum Buffer Filling (MBF) algorithm—which aims to increase the number of satisfied users in video streaming services provided by wireless networks. The MBF algorithm uses the current buffer level at the client side and the radio channel conditions, which are reported to the network by the client, as well as the bitrate of the requested video segment. The proposed scheduling strategy can also fulfill different satisfaction criteria, since it can be tuned to maximize the numbers of users with high QoE levels or to minimize the number of users with low QoE levels. A simulation framework was developed, considering a Long Term Evolution (LTE) scenario, in order to assess the performance of the proposed scheduling scheme and to compare it with other well-known scheduling solutions. The results show the superior performance achieved by the proposed technique, in terms of the number of satisfied and unsatisfied users.

Forecasting Video QoE With Deep Learning From Multivariate Time-Series

“…We propose a deep learning sequence model that forecasts QoE metrics at the next time step and before occurring on the client's screen. The use cases of our proposed approach are video streaming applications that can benefit from QoE forecasting, such as fault diagnosis in packet video networks [8], anomaly detection in CDN and wireless networks [7][6], and resource scheduling in wireless networks for adaptive DASH delivery [9].…”

An automated model for the assessment of QoE of adaptive video streaming over wireless networks

“…Towards avoiding high-cost tests based on laboratory experiments, objective quality models must predict QoE based on objective QoS parameters. Moreover, the increasing demand for video streaming over the Internet requires new approaches (e.g., data-driven QoE models) for the processing of massive data [12,13,14]. Poojary S. et al [15] developed a system that analyzes QoE for adaptive video streaming over wireless networks.…”

“…• QoE assessment in the wireless scenario. [6,7,12,34,35] focused merely on bandwidth parameter to find the assessment of QoE for HTTP adaptive streaming. HTTP uses TCP, however, other QoS parameters (e.g., delay and packet loss) are also affected the performance, to address this limitation of existing models we develop an automated streaming model based on more parameters, namely (delay and packet loss).…”

A survey on QoE-oriented wireless resources scheduling

“…It is noteworthy to mention that even though the previous scheduling strategies exploit the higher throughputs that can be attained by some users, this approach might still be inefficient -for instance, prioritizing the user that is experiencing the highest throughput may not imply that the respective buffer level will have a great increment (e.g., this user could be requesting a very high quality video); therefore, it might be more efficient to serve other users which can store a higher amount of playout time in their buffers, in order to better obviate the occurrence of stalls. This approach is followed by the scheduling algorithm introduced in (Rodrigues et al, 2018), namely by considering explicitly the predicted buffer level variation of the user i, ∆B i , according to the resources that could be allocated to this user:…”