The increasing popularity of streaming video is a cause of concern for the stability of the internet because most streaming video content is currently delivered via UDP without any end-to-end congestion control. Since the internet relies on end systems implementing transmit rate regulation, there has recently been significant interest in congestion control mechanisms that are both fair to TCP and effective in delivering real-time streams. Streaming video over the internet requires dealing with bandwidth and delay that vary over time. Many video streaming applications address this problem by adapting the quality of the scalable video. But it produces poor quality service, and sending data on this channel results in buffering time. To trounce these issues, this paper proposed optimized bandwidth estimation for adaptive video streaming systems using the WLBWO algorithm. Originally, the input video is compressed by using the UHE algorithm. Next, the system proposes a KEECC to securely transfer the data. Then, the encrypted data is sent to the receiver via a multipath channel. Before sending the data to the receiver, the bandwidth is estimated by using the WLBWO. Finally, the inverse process is performed. Extensive experimental results showed the effectiveness of the proposed system than conventional methods.

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Dynamic Adaptive Streaming over HTTP (DASH) is an emerging solution that aims to standardize existing proprietary streaming systems. DASH specification defines the media presentation description (MPD), which describes a list of available content, URL addresses, and the segment format. High bandwidth demands in interactive streaming applications pose challenges in efficiently utilizing the available bandwidth. In this paper, a novel Relative Strength Index (RSI) with Geometric mean (GM) namely RSI-GM is proposed for estimating available bandwidth for DASH. The proposed work starts by taking the video as an input at the transmitter side and then the video compression is performed using the TRLE. Then MD5 hashing-based AES encryption is applied to the compressed video data to provide data security. Then RSI-GM is proposed to estimate the available bandwidth for DASH. Finally, after estimation, the bitrate for estimated bandwidth is selected optimally using the Improved Shark Smell Optimization (ISSO) algorithm.