Fuzzy-Based MEC-Assisted Video Adaptation Framework for HTTP Adaptive Streaming

As the demand for high-quality video streaming applications continues to rise, multi-access edge computing (MEC)-assisted streaming schemes have emerged as a viable solution within the context of HTTP adaptive streaming (HAS). These schemes aim to enhance both quality of experience (QoE) and utilization of network resources. HAS faces a significant challenge when applied to mobile cellular networks. Designing a HAS scheme that fairly allocates bitrates to users ensures a high QoE and optimizes bandwidth utilization remains a challenge. To this end, we designed an MEC- and client-assisted adaptation framework for HAS, facilitating collaboration between the edge and client to enhance users’ quality of experience. The proposed framework employs fuzzy logic at the user end to determine the upper limit for the video streaming rate. On the MEC side, we developed an integer nonlinear programming (INLP) optimization model that collectively enhances the QoE of video clients by considering the upper limit set by the client. Due to the NP-hardness of the problem, we utilized a greedy algorithm to efficiently solve the quality adaptation optimization problem. The results demonstrate that the proposed framework, on average, (i) improves users’ QoE by 30%, (ii) achieves a fair allocation of bitrates by 22.6%, and (iii) enhances network utilization by 4.2% compared to state-of-the-art approaches. In addition, the proposed approach prevents playback interruptions regardless of the client’s buffer size and video segment duration.