Toward Timeliness-Enhanced Loss Recovery for Large-Scale Live Streaming

Bo Wu, Tong Li, cheng luo, Xu Yan, FuYu Wang, Xinle Du, Ke Xu

Abstract:

Due to the limited permissions for upgrading dual-side (i.e., server-side and client-side) loss tolerance schemes from the perspective of CDN vendors in a multi-supplier market, modern large-scale live streaming services are still using the automatic-repeat-request (ARQ) based paradigm for loss recovery, which only requires server-side modifications. In this paper, we first conduct a large-scale measurement study with a collection of up to 50 million live streams. We find that loss shows dynamics and live streaming contains frequent on-off mode switching in the wild. We further find that the recovery latency, enlarged by the ubiquitous retransmission loss, is a critical factor affecting client-side QoE (e.g., video freezing) of live streaming.
We then propose an enhanced recovery mechanism called AutoRec, which can transform the disadvantages of on-off mode switching into an advantage for reducing loss recovery latency without any modifications on the client side.
AutoRec also adopts an online learning-based scheduler to fit the dynamics of loss, balancing the tradeoff between the recovery latency and the incurred overhead. We implement AutoRec upon QUIC and evaluate it via both testbed and real-world deployments of commercial services. The experimental results demonstrate the practicability and profitability of AutoRec, in which the 95th-percentile times and duration of client-side video freezing can be lowered by 34.1\% and 16.0\%, respectively.

Primary Subject Area: [Systems] Transport and Delivery

Secondary Subject Area: [Systems] Transport and Delivery

Relevance To Conference: AutoRec contributes significantly to multimedia, particularly in the context of live-streaming services. It can enhance the recovery mechanism for lost packets during transmission and optimize the timeliness of loss recovery without requiring client-side modifications. Moreover, AutoRec intelligently balances the timeliness of loss recovery and overhead by deciding when to activate loss reinjection and using an online learning-based scheduler to determine the amount of each loss reinjection. In practical terms, the implementation of AutoRec on QUIC and its evaluation through both testbed and real-world deployments have demonstrated its effectiveness. It has been shown to significantly reduce the times and duration of client-side video freezing without introducing non-trivial overhead, thereby enhancing the Quality of Experience (QoE) in multimedia live-streaming scenarios.

Submission Number: 3908