Framework for video streaming and optimized resource usage in cloud

Abstract

With the recent surge in digital imaging devices, video streaming services have seen enormous growth nowadays. This leads to the growth of cloud based video streaming services. Video streaming systems are growing in such a manner that, video based services will be the leading paradigm in the internet based ecosystem in a span of ten years. Cloud based video services cater to the needs of the end user based on their requirements, through video as a Service. In video as a service, the major objectives are to provide improvement in video quality, smoothness in video streaming in a fast pace and to satisfy the users expectation at a minimal cost. To achieve these objectives, variation in the devices used by the end user, unstable connectivity and unexpected hike in the request for resource access are some of the major hurdles. These hurdles affect fast paced research areas of On-demand Video Service, Online Video Conference, Telepresence, Video Surveillance, and Content Based Video Retrieval heavily. Videos of very large size require high computing power and storage. Storing these videos, processing and streaming them creates more workload when compared to small videos. This also leads to compensation of quality in the streaming process. When the video is being streamed video quality has to be modified according to the type of device the user utilizes. This takes into consideration the size of the screen, bandwidth of the user, memory capacity, and energy requirement of the device. The next issue is on the allocation of the resources for the streaming of the video. Based on the load, the resources are allocated. Allocation of resources when not done optimally degrades the utilization rate and affects the users Quality of Experience(QoE) due to delay in the streamed video. The last thing that needs to be considered is the fact that at times, a single event is being streamed universally. newline