5 research outputs found
QoE-Aware Resource Allocation For Crowdsourced Live Streaming: A Machine Learning Approach
In the last decade, empowered by the technological advancements of mobile devices
and the revolution of wireless mobile network access, the world has witnessed an
explosion in crowdsourced live streaming. Ensuring a stable high-quality playback
experience is compulsory to maximize the viewers’ Quality of Experience and the
content providers’ profits. This can be achieved by advocating a geo-distributed cloud
infrastructure to allocate the multimedia resources as close as possible to viewers, in
order to minimize the access delay and video stalls.
Additionally, because of the instability of network condition and the heterogeneity of
the end-users capabilities, transcoding the original video into multiple bitrates is
required. Video transcoding is a computationally expensive process, where generally a
single cloud instance needs to be reserved to produce one single video bitrate
representation. On demand renting of resources or inadequate resources reservation
may cause delay of the video playback or serving the viewers with a lower quality. On
the other hand, if resources provisioning is much higher than the required, the
extra resources will be wasted.
In this thesis, we introduce a prediction-driven resource allocation framework, to
maximize the QoE of viewers and minimize the resources allocation cost. First, by
exploiting the viewers’ locations available in our unique dataset, we implement a machine learning model to predict the viewers’ number near each geo-distributed cloud
site. Second, based on the predicted results that showed to be close to the actual values,
we formulate an optimization problem to proactively allocate resources at the viewers’
proximity. Additionally, we will present a trade-off between the video access delay and
the cost of resource allocation.
Considering the complexity and infeasibility of our offline optimization to respond to
the volume of viewing requests in real-time, we further extend our work, by introducing
a resources forecasting and reservation framework for geo-distributed cloud sites. First,
we formulate an offline optimization problem to allocate transcoding resources at the
viewers’ proximity, while creating a tradeoff between the network cost and viewers
QoE. Second, based on the optimizer resource allocation decisions on historical live
videos, we create our time series datasets containing historical records of the optimal
resources needed at each geo-distributed cloud site. Finally, we adopt machine learning
to build our distributed time series forecasting models to proactively forecast the exact
needed transcoding resources ahead of time at each geo-distributed cloud site.
The results showed that the predicted number of transcoding resources needed in each
cloud site is close to the optimal number of transcoding resources
A quality of experience approach in smartphone video selection framework for energy efficiency
Online video streaming is getting more common in the smartphone device nowadays.
Since the Corona Virus (COVID-19) pandemic hit all human across the globe in 2020,
the usage of online streaming among smartphone user are getting more vital.
Nevertheless, video streaming can cause the smartphone energy to drain quickly
without user to realize it. Also, saving energy alone is not the most significant issues
especially if with the lack of attention on the user Quality of Experience (QoE). A
smartphones energy management is crucial to overcome both of these issues. Thus, a
QoE Mobile Video Selection (QMVS) framework is proposed. The QMVS
framework will govern the tradeoff between energy efficiency and user QoE in the
smartphone device. In QMVS, video streaming will be using Dynamic Video Attribute
Pre-Scheduling (DVAP) algorithm to determine the energy efficiency in smartphone
devices. This process manages the video attribute such as brightness, resolution, and
frame rate by turning to Video Content Selection (VCS). DVAP is handling a set of
rule in the Rule Post-Pruning (RPP) method to remove an unused node in list tree of
VCS. Next, QoE subjective method is used to obtain the Mean Opinion Score (MOS)
of users from a survey experiment on QoE. After both experiment results (MOS and
energy) are established, the linear regression technique is used to find the relationship
between energy consumption and user QoE (MOS). The last process is to analyze the
relationship of VCS results by comparing the DVAP to other recent video streaming
applications available. Summary of experimental results demonstrate the significant
reduction of 10% to 20% energy consumption along with considerable acceptance of
user QoE. The VCS outcomes are essential to help users and developer deciding which
suitable video streaming format that can satisfy energy consumption and user QoE
QoE-aware distributed cloud-based live streaming of multisourced multiview videos
Video streaming is one of the most prevailing and bandwidth consuming Internet applications today. Advancements in technology and prevalence of video capturing devices result in massive multi-sourced (aka crowdsourced) live video broadcasting over the Internet. A single scene may be captured by multiple spectators from different angles (views), enabling an opportunity for interactive multiview video by integrating these individually captured views. Such multi-sourced multiview video offers more realistic and immersive experience of a scene. In this paper, we present a Quality of Experience (QoE) driven, cost effective Crowdsourced Multiview Live Streaming (CMLS) system. The CMLS aims to minimize the overall system cost by selecting optimal cloud site for video transcoding and the number of representations, based on the view popularity and viewer's available bandwidth. In addition, we present a QoE metric considering delay and received video quality. We formulate the selection of optimal cloud site and number of representations to meet the required QoE as a resource allocation problem using Integer Programming (IP). Moreover, we present a Greedy Minimal Cost (GMC) algorithm to perform resource allocation efficiently. We use real live video traces collected from three large-scale live video providers (Twitch.tv, YouTube Live, and YouNow) to evaluate our proposed strategy. We evaluate the GMC algorithm considering the overall cost, QoE, video quality, and average latency between viewers and transcoding location. We compare our results with the optimal solution and the state-of-the art policy used in a popular video steaming system. Our results demonstrate that the GMC achieves near optimal results and substantially outperforms the state-of-the art policy.This publication was made possible by NPRP grant # [ 8-519-1-108 ] from the Qatar National Research Fund (a member of Qatar Foundation). We are thankful to the Denny Stohr for providing YouNow dataset. The findings achieved herein are solely the responsibility of the author[s].Scopu
Exploring the Emerging Domain of Research on Video Game Live Streaming in Web of Science: State of the Art, Changes and Trends
In recent years, interest in video game live streaming services has increased as a new communication instrument, social network, source of leisure, and entertainment platform for millions of users. The rise in this type of service has been accompanied by an increase in research on these platforms. As an emerging domain of research focused on this novel phenomenon takes shape, it is necessary to delve into its nature and antecedents. The main objective of this research is to provide a comprehensive reference that allows future analyses to be addressed with greater rigor and theoretical depth. In this work, we developed a meta-review of the literature supported by a bibliometric performance and network analysis (BPNA). We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) protocol to obtain a representative sample of 111 published documents since 2012 and indexed in the Web of Science. Additionally, we exposed the main research topics developed to date, which allowed us to detect future research challenges and trends. The findings revealed four specializations or subdomains: studies focused on the transmitter or streamer; the receiver or the audience; the channel or platform; and the transmission process. These four specializations add to the accumulated knowledge through the development of six core themes that emerge: motivations, behaviors, monetization of activities, quality of experience, use of social networks and media, and gender issues