10,208 research outputs found
A Kalman Filter based Low Complexity Throughput Prediction Algorithm for 5G Cellular Networks
Throughput Prediction is one of the primary preconditions for the
uninterrupted operation of several network-aware mobile applications, namely
video streaming. Recent works have advocated using Machine Learning (ML) and
Deep Learning (DL) for cellular network throughput prediction. In contrast,
this work has proposed a low computationally complex simple solution which
models the future throughput as a multiple linear regression of several present
network parameters and present throughput. It then feeds the variance of
prediction error and measurement error, which is inherent in any measurement
setup but unaccounted for in existing works, to a Kalman filter-based
prediction-correction approach to obtain the optimal estimates of the future
throughput. Extensive experiments across seven publicly available 5G throughput
datasets for different prediction window lengths have shown that the proposed
method outperforms the baseline ML and DL algorithms by delivering more
accurate results within a shorter timeframe for inferencing and retraining.
Furthermore, in comparison to its ML and DL counterparts, the proposed
throughput prediction method is also found to deliver higher QoE to both
streaming and live video users when used in conjunction with popular Model
Predictive Control (MPC) based adaptive bitrate streaming algorithms.Comment: 13 pages, 14 figure
MANSY: Generalizing Neural Adaptive Immersive Video Streaming With Ensemble and Representation Learning
The popularity of immersive videos has prompted extensive research into
neural adaptive tile-based streaming to optimize video transmission over
networks with limited bandwidth. However, the diversity of users' viewing
patterns and Quality of Experience (QoE) preferences has not been fully
addressed yet by existing neural adaptive approaches for viewport prediction
and bitrate selection. Their performance can significantly deteriorate when
users' actual viewing patterns and QoE preferences differ considerably from
those observed during the training phase, resulting in poor generalization. In
this paper, we propose MANSY, a novel streaming system that embraces user
diversity to improve generalization. Specifically, to accommodate users'
diverse viewing patterns, we design a Transformer-based viewport prediction
model with an efficient multi-viewport trajectory input output architecture
based on implicit ensemble learning. Besides, we for the first time combine the
advanced representation learning and deep reinforcement learning to train the
bitrate selection model to maximize diverse QoE objectives, enabling the model
to generalize across users with diverse preferences. Extensive experiments
demonstrate that MANSY outperforms state-of-the-art approaches in viewport
prediction accuracy and QoE improvement on both trained and unseen viewing
patterns and QoE preferences, achieving better generalization.Comment: This work has been submitted to the IEEE Transactions on Mobile
Computing for possible publication. Copyright may be transferred without
notice, after which this version may no longer be accessibl
QoE-Based Low-Delay Live Streaming Using Throughput Predictions
Recently, HTTP-based adaptive streaming has become the de facto standard for
video streaming over the Internet. It allows clients to dynamically adapt media
characteristics to network conditions in order to ensure a high quality of
experience, that is, minimize playback interruptions, while maximizing video
quality at a reasonable level of quality changes. In the case of live
streaming, this task becomes particularly challenging due to the latency
constraints. The challenge further increases if a client uses a wireless
network, where the throughput is subject to considerable fluctuations.
Consequently, live streams often exhibit latencies of up to 30 seconds. In the
present work, we introduce an adaptation algorithm for HTTP-based live
streaming called LOLYPOP (Low-Latency Prediction-Based Adaptation) that is
designed to operate with a transport latency of few seconds. To reach this
goal, LOLYPOP leverages TCP throughput predictions on multiple time scales,
from 1 to 10 seconds, along with an estimate of the prediction error
distribution. In addition to satisfying the latency constraint, the algorithm
heuristically maximizes the quality of experience by maximizing the average
video quality as a function of the number of skipped segments and quality
transitions. In order to select an efficient prediction method, we studied the
performance of several time series prediction methods in IEEE 802.11 wireless
access networks. We evaluated LOLYPOP under a large set of experimental
conditions limiting the transport latency to 3 seconds, against a
state-of-the-art adaptation algorithm from the literature, called FESTIVE. We
observed that the average video quality is by up to a factor of 3 higher than
with FESTIVE. We also observed that LOLYPOP is able to reach a broader region
in the quality of experience space, and thus it is better adjustable to the
user profile or service provider requirements.Comment: Technical Report TKN-16-001, Telecommunication Networks Group,
Technische Universitaet Berlin. This TR updated TR TKN-15-00
Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video: A Large-Scale Dataset and a Deep Learning Model
Omnidirectional video enables spherical stimuli with the viewing range. Meanwhile, only the viewport region of omnidirectional
video can be seen by the observer through head movement (HM), and an even
smaller region within the viewport can be clearly perceived through eye
movement (EM). Thus, the subjective quality of omnidirectional video may be
correlated with HM and EM of human behavior. To fill in the gap between
subjective quality and human behavior, this paper proposes a large-scale visual
quality assessment (VQA) dataset of omnidirectional video, called VQA-OV, which
collects 60 reference sequences and 540 impaired sequences. Our VQA-OV dataset
provides not only the subjective quality scores of sequences but also the HM
and EM data of subjects. By mining our dataset, we find that the subjective
quality of omnidirectional video is indeed related to HM and EM. Hence, we
develop a deep learning model, which embeds HM and EM, for objective VQA on
omnidirectional video. Experimental results show that our model significantly
improves the state-of-the-art performance of VQA on omnidirectional video.Comment: Accepted by ACM MM 201
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