{'fraction_non_alphanumeric': 0.01820464532328939, 'fraction_numerical': 0.0, 'mean_word_length': 5.586776859504132, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Edge Computing Assisted Adaptive Streaming Scheme for Mobile Networks

Various video streaming platforms are responsible for continuously generating a majority of Internet traffic. The promising solution for the smooth video streaming services is to use the HTTP adaptive streaming (HAS). To guarantee a high quality of experience (QoE), the clients using the HAS dedicate the bitrate adaptation for the upcoming video segments. However, the client-driven approach causes degradation in QoE, inefficient resource utilization, and unfair bitrate allocation when multiple clients stream a video over the same access network. Edge computing-assisted adaptive streaming gives an opportunity to jointly optimize the QoE of clients, resource utilization, and fairness among clients by shifting the adaptation intelligence from the clients to the edge cloud. In this paper, we first present an adaptive streaming framework taking advantage of the capabilities of multi-access edge computing (MEC). Next, we design an optimization model that jointly considers the main influencing factors in QoE and fairness among clients. The proposed scheme formulates the joint optimization problem with the various constraints by considering the correlation between QoE and fairness. To efficiently solve the joint optimization problem, we propose a greedy-based bitrate allocation algorithm for multiple clients. The results from the performance evaluation show that the proposed scheme can improve the QoE of clients and resource utilization compared with the existing schemes and minimize the loss in fairness.