Today, mobile live streaming is gaining a rapid growth in use, which refers to watching the media content recorded and broadcast in real time on mobile devices. In live streaming process, each video segment must go through recording, encoding, uploading, transcoding, publishing, downloading, decoding before playback. The ingest algorithm inside the streamer decides the upload bitrate, while the adaptive bitrate (ABR) algorithm inside the player determines the download bitrate. Thanks to the chunked CMAF standard, each segment is split into smaller chunks that can be independently encoded, transferred, decoded and played. It is of great help to quantify the impact of chunk-level characteristics on mobile live streaming performance. In this paper, we establish a tandem queuing model to describe the whole streaming system. Based on the model, we respectively characterize rebuffering probability, rebuffering count, streaming latency, and average bitrate, analyzing the impact of chunk upload and download rates, upload and download time variances, startup threshold and chunk length on them. From analysis results, we propose insights and recommendations for bitrate adaptation in mobile live streaming and design simple heuristic ingest and ABR algorithms leveraging them. Extensive simulations verify the insights as well as effectiveness of designed algorithms.

Nowadays, optical circuit switching is becoming an increasingly favored technology in scaling data center networks for its definitive advantages in data rate, power consumption, and device cost. Concurrently, reducing coflow completion time (CCT) is of great significance for improving application-level performance. However, minimizing CCT in circuit switched networks is totally different from that in traditional packet switched networks due to port constraints and circuit reconfiguration delays. To address this issue, this article proposes Grouped Optimization-based Scheduling (GOS), a CCT minimization algorithm for circuit switched networks integrating circuit and coflow scheduling. We first formalize the CCT minimization problem into a 0-1 programming problem, then relax and solve the problem in 2 steps to obtain the coflow order and flow grouping decisions on each circuit. Thus intra-group reconfiguration delays are saved, and small coflows can be prioritized at the group level. Theoretical analysis proves GOS is a 4-approximation algorithm in average CCT. To reduce computing overheads, we further propose a heuristic approximation algorithm. Extensive simulations show that the heuristic algorithm has satisfactory CCT performance (0.12× Varys, 0.36× Sunflow) as well as high throughput (16.74× Varys, 1.32× Sunflow), and well adapts to a wide range of reconfiguration delays and algorithm decision time.