Quality-Aware Massive Content Delivery in Digital Twin-Enabled Edge Networks

Massive content delivery will become one of the most prominent tasks of future B5G/6G communication. However, various multimedia ap-plications possess huge differences in terms of ob-ject oriented (i.e., machine or user) and corresponding quality evaluation metric, which will significantly im-pact the design of encoding or decoding within con-tent delivery strategy. To get over this dilemma, we firstly integrate the digital twin into the edge networks to accurately and timely capture Quality-of-Decision (QoD) or Quality-of-Experience (QoE) for the guid-ance of content delivery. Then, in terms of machine -centric communication, a QoD-driven compression mechanism is designed for video analytics via tem-porally lightweight frame classification and spatially uneven quality assignment, which can achieve a bal-ance among decision-making, delivered content, and encoding latency. Finally, in terms of user-centric communication, by fully leveraging haptic physical properties and semantic correlations of heterogeneous streams, we develop a QoE-driven video enhance-ment scheme to supply high data fidelity. Numerical results demonstrate the remarkable performance im-provement of massive content delivery.