prem1409 / VideoStreamingMechanism

we propose a new technique to balance the traffic for serving multiple users without delay in spite off all the computation power required by video transcoding. We aim towards optimizing the Quality of Experience (QoE) by considering dynamic adaptive video streaming techniques. One issue that can the QoE is the workload on content distribution servers. Thus, we plan to reduce the load on the server’s workload by designing an architecture that will manage the load on the servers without compromising the quality of the video to be streamed.

Online Video Streaming Mechanism with Servers Deployed Over Cloud

We propose a new technique to balance the traffic for serving multiple users without delay in spite off all the computation power required by video transcoding. We aim towards optimizing the Quality of Experience (QoE) by considering dynamic adaptive video streaming techniques. One issue that can the QoE is the workload on content distribution servers. Thus, we plan to reduce the load on the server’s workload by designing an architecture that will manage the load on the servers without compromising the quality of the video to be streamed.
Online Video Streaming is multimedia that is constantly received by and presented to an end-user while being delivered by a provider. We have deployed our application over Google Cloud Platform.
This folder contains the code used in our final project for MM802 Multimedia Communications whose course intructor is Dr. MOHAMMED ELMORSY.

Project

This is a full application made for video streaming.
The technology stack includes
FrontEnd: HTML5,CSS3, AngularJS
BackEnd: Python(Django REST Framework)
Database: MySQL
Deployment: Google Cloud Platform

Prerequisites for this application are
Google Cloud Platform Account
Knowledge about Docker/Kubernetes

Install Docker using the following link:
Ubuntu: https://phoenixnap.com/kb/how-to-install-docker-on-ubuntu-18-04
Windows 10: https://docs.docker.com/toolbox/toolbox_install_windows/
MAC OS: https://docs.docker.com/docker-for-mac/install/

Dataset

We have used UGC Dataset of YouTube is a sampling from thousands of User Generated Content(UGC) as uploaded to YouTube distributed under the Creative Commons license. The dataset comprises of around 1500 video clips consisting of various categories like Animation, Cover Song, Gaming, HDR, How-To, Lecture, Live Music, Lyric Video, Music Video, News Clip, Sports, Television Clip, Vertical Video, Vlog, and VR. It also consists of various resolutions 360P, 480P, 720P, and 1080P for all categories (except for HDR and VR)
Dataset Link: https://media.withyoutube.com/

Overall Architecture Flow

The overall architecture for our system is shown below:

Authors:

Name	github handle
Jatin Dawar	@jatin008
Prem Raheja	@prem1409
Utkarsh Vashisth	@uvashisth
Vaibhav Rakheja	@vaibhavrakheja11