269 research outputs found
Intelligent Algorithm for Enhancing MPEG-DASH QoE in eMBMS
[EN] Multimedia streaming is the most demanding and bandwidth hungry application in today¿s world of Internet. MPEG-DASH as a video technology standard is designed for delivering live or on-demand streams in Internet to deliver best quality content with the fewest dropouts and least possible buffering. Hybrid architecture of DASH and eMBMS has attracted a great attention from the telecommunication industry and multimedia services. It is deployed in response to the immense demand in multimedia traffic. However, handover and limited available resources of the system affected on dropping segments of the adaptive video streaming in eMBMS and it creates an adverse impact on Quality of Experience (QoE), which is creating trouble for service providers and network providers towards delivering the service. In this paper, we derive a case study in eMBMS to approach to provide test measures evaluating MPEG-DASH QoE, by defining the metrics are influenced on QoE in eMBMS such as bandwidth and packet loss then we observe the objective metrics like stalling (number, duration and place), buffer length and accumulative video time. Moreover, we build a smart algorithm to predict rate of segments are lost in multicast adaptive video streaming. The algorithm deploys an estimation decision regards how to recover the lost segments. According to the obtained results based on our proposal algorithm, rate of lost segments is highly decreased by comparing to the traditional approach of MPEG-DASH multicast and unicast for high number of users.This work has been partially supported by the Postdoctoral Scholarship Contratos Postdoctorales UPV 2014 (PAID-10-14) of the Universitat Politècnica de València , by the Programa para la Formación de Personal Investigador (FPI-2015-S2-884) of the Universitat Politècnica de València , by the Ministerio de Economía y Competitividad , through the Convocatoria 2014. Proyectos I+D - Programa Estatal de Investigación Científica y Técnica de Excelencia in the Subprograma Estatal de Generación de Conocimiento , project TIN2014-57991-C3-1-P and through the Convocatoria 2017 - Proyectos I+D+I - Programa Estatal de Investigación, Desarrollo e Innovación, convocatoria excelencia (Project TIN2017-84802-C2-1-P).Abdullah, MT.; Jimenez, JM.; Canovas Solbes, A.; Lloret, J. (2017). Intelligent Algorithm for Enhancing MPEG-DASH QoE in eMBMS. Network Protocols and Algorithms. 9(3-4):94-114. https://doi.org/10.5296/npa.v9i3-4.12573S9411493-
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
Dissecting the performance of VR video streaming through the VR-EXP experimentation platform
To cope with the massive bandwidth demands of Virtual Reality (VR) video streaming, both the scientific community and the industry have been proposing optimization techniques such as viewport-aware streaming and tile-based adaptive bitrate heuristics. As most of the VR video traffic is expected to be delivered through mobile networks, a major problem arises: both the network performance and VR video optimization techniques have the potential to influence the video playout performance and the Quality of Experience (QoE). However, the interplay between them is neither trivial nor has it been properly investigated. To bridge this gap, in this article, we introduce VR-EXP, an open-source platform for carrying out VR video streaming performance evaluation. Furthermore, we consolidate a set of relevant VR video streaming techniques and evaluate them under variable network conditions, contributing to an in-depth understanding of what to expect when different combinations are employed. To the best of our knowledge, this is the first work to propose a systematic approach, accompanied by a software toolkit, which allows one to compare different optimization techniques under the same circumstances. Extensive evaluations carried out using realistic datasets demonstrate that VR-EXP is instrumental in providing valuable insights regarding the interplay between network performance and VR video streaming optimization techniques
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