The increasingly prevalent livecast streaming causes expensive bandwidth costs and delivery capacity challenges for the content delivery network (CDN) service. As an emerging paradigm, edge computing offers new opportunities to address this issue. The existing works are limited to the data volume pricing model. In contrast, we focus on the 95th-percentile pricing model, which is adopted by many large-scale livecast systems. We propose a Cost-aware Edge-assisted Livecast system (CEL) to minimize the bandwidth cost, consisting of two components: 1) the Shapley values are leveraged to model the actual bandwidth costs for the CDN and edge servers in different time slots, together with acceleration technologies for fast Shapley value estimation and 2) a greedy request scheduling algorithm with theoretical guarantees is proposed to solve the online request scheduling problem, which is NP-hard. Based on real-world data from an operational livecast system, our experiments demonstrate thatCELis time-efficient and achieves at least 14.81% bandwidth cost savings compared with four state-of-the-art methods.

Volumetric videos offer viewers more immersive experiences, enabling a variety of applications. However, state-of-the-art streaming systems still need hundreds of Mbps bandwidth to transmit volumetric videos, exceeding the common bandwidth capabilities of mobile devices. We find a research gap in reusing inter-frame redundant information to reduce bandwidth consumption, while the existing inter-frame compression methods rely on the so-calledexplicit correlation, i.e., the redundancy from the same/adjacent locations in the previous frame, which does not apply to highly dynamic frames or dynamic viewports. This paper introduces a new concept calledimplicit correlation, i.e., the consistency of topological structures, which stably exists in dynamic frames and is beneficial for reducing bandwidth consumption. We design a mobile volumetric video streaming system Hermes consisting of an implicit correlation encoder to reduce bandwidth consumption and a hybrid streaming method that adapts to dynamic viewports. Experiments on public datasets show that Hermes achieves a frame rate of 30+ FPS over daily networks and on commodity smartphones, with at least 3.64× and 3.34× improvement compared with two state-of-the-art baselines, respectively.

Volumetric videos offer viewers more immersive experiences, enabling a variety of applications. However, state-of-the-art streaming systems still need hundreds of Mbps, exceeding the common bandwidth capabilities of mobile devices. We find a research gap in reusing inter-frame redundant information to reduce bandwidth consumption, while the existing inter-frame compression methods rely on the so-called explicit correlation, i.e., the redundancy from the same/adjacent locations in the previous frame, which does not apply to highly dynamic frames or dynamic viewports. This work introduces a new concept called implicit correlation, i.e., the consistency of topological structures, which stably exists in dynamic frames and is beneficial for reducing bandwidth consumption. We design a mobile volumetric video streaming system Hermes consisting of an implicit correlation encoder to reduce bandwidth consumption and a hybrid streaming method that adapts to dynamic viewports. Experiments show that Hermes achieves a frame rate of 30+ FPS over daily networks and on commodity smartphones, with at least 3.37x improvement compared with two baselines.