Effective client-driven three-level rate adaptation (TLRA) approach for adaptive HTTP streaming

Multimedia streaming allows consumers to view multimedia content anywhere. However, quality of service is a major concern amid heightened levels of network traffic caused by increasing user demand. Accordingly, media streaming technology is adopting a new paradigm: adaptive HTTP streaming (AHS). AHS is widely used for real-time streaming content delivery in the Internet environment. In streaming, selection of appropriate bitrate is crucial for adapting media rate to network variations and client processing capabilities while ensuring optimal service for the consumer. We evaluate a proposed client-driven three-level optimized rate adaptation algorithm for adaptive HTTP media streaming. In the first stage, the algorithm chooses a suitable starting bitrate close to the available channel capacity. Next, the algorithm monitors the client parameters in real time, precisely detecting network variations and choosing a likely available bit representation for the next download segment. The algorithm controls and minimizes the effects of buffer stalls and overflow resulting from the brief network variations occurring between consecutive segments. The proposed algorithm is implemented in Dynamic Adaptive Streaming over HTTP (DASH) player and its performance compared to that of commercially available Gstreamer-based HTTP Live Streaming (HLS) and DASH players which use conventional segment fetch time–based adaptation and throughput-based adaptation algorithms respectively. This evaluation uses a real-time cloud server client and test bed streaming setup. The resulting analysis shows that the client-driven three-level rate adaptation (TLRA) approach allows adaptive streaming clients to maximize use of end-to-end network capacity, delivering an ideal user experience by precisely predicting network variations and rapidly adapting to available bandwidth, minimizing rebuffering events and bitrate level changes.