In multi-party interactive live streaming, each user can act as both the sender and the receiver of a live video stream. Designing adaptive bitrate (ABR) algorithm for such applications poses three challenges: (i) due to the interaction requirement among the users, the playback buffer has to be kept small to reduce the end-to-end delay; (ii) the algorithm needs to decide what is the bitrate to receive and what is the set of bitrates tosend; (iii) the delay and quality requirements between each pair of users may differ, for instance, depending on whether the pair is interacting directly with each other. To address these challenges, we first develop a quality of experience (QoE) model for multi-party live streaming applications. Based on this model, we designMultiLive, an adaptive bitrate control algorithm for the multi-party scenario. MultiLive models the many-to-many ABR selection problem as a non-linear programming problem. Solving the non-linear programming equation yields the target bitrate for each pair of sender-receiver. To alleviate system errors during the modeling and measurement process, we update the target bitrate through the buffer feedback adjustment. To address the throughput limitation of the uplink, we cluster the ideal streams into a few groups, and aggregate these streams through scalable video coding for transmissions. We also deploy the algorithm on a commercial live streaming platform that provides such services for more than 2300 users. The experimental results show that MultiLive outperforms the fixed bitrate algorithm, with 2-$5\times $improvement in average QoE. Furthermore, the end-to-end delay is reduced to around 100 ms, much lower than the 400 ms threshold recommended for video conferencing.

Delay-sensitive multimedia streaming applications require their data to be delivered before a deadline to be useful. The data transmitted by these applications can usually be partitioned into blocks with different priorities, assigned based on the impact of a block on the Quality of Experience (QoE) if it misses its delivery deadline. Meet their deadline requirements is challenging due to the dynamics of the network and these applications' high demand on network resources. To encourage the research community to address this challenge, we organize the "Meet Deadline Requirements" Grand Challenge at ACM Multimedia 2021. This grand challenge provides a simulation platform onto which the participants can implement their block scheduler and bandwidth estimator and then benchmark against each other using a common set of application traces and network traces.

In multi-party interactive live streaming, each user can act as both the sender and the receiver of a live video stream. Designing adaptive bitrate (ABR) algorithm for such applications poses three challenges: (i) due to the interaction requirement among the users, the playback buffer has to be kept small to reduce the end-to-end delay; (ii) the algorithm needs to decide what is the bitrate to receive and what is the set of bitrates to send; (iii) the delay and quality requirements between each pair of users may differ, for instance, depending on whether the pair is interacting directly with each other. To address these challenges, we first develop a quality of experience (QoE) model for multi-party live streaming applications. Based on this model, we design MultiLive, an adaptive bitrate control algorithm for the multi-party scenario. MultiLive models the many-to-many ABR selection problem as a non-linear programming problem. Solving the non-linear programming equation yields the target bitrate for each pair of sender-receiver. To alleviate system errors during the modeling and measurement process, we update the target bitrate through the buffer feedback adjustment. To address the throughput limitation of the uplink, we cluster the ideal streams into a few groups, and aggregate these streams through scalable video coding for transmissions. We conduct extensive trace-driven simulations to evaluate the algorithm. The experimental results show that MultiLive outperforms the fixed bitrate algorithm, with 2-5× improvement in average QoE. Furthermore, the end-to-end delay is reduced to around 100 ms, much lower than the 400 ms threshold recommended for video conferencing.

Live video streaming delivery over Dynamic Adaptive Video Streaming (DASH) is challenging as it requires low end-to-end latency, is more prone to stall, and the receiver has to decide online which representation at which bitrate to download and whether to adjust the playback speed to control the latency. To encourage the research community to come together to address this challenge, we organize the Live Video Streaming Grand Challenge at ACM Multimedia 2019. This grand challenge provides a simulation platform onto which the participants can implement their adaptive bitrate (ABR) logic and latency control algorithm, and then benchmark against each other using a common set of video traces and network traces. The ABR algorithms are evaluated using a common Quality-of- Experience (QoE) model that accounts for playback bitrate, latency constraint, frame-skipping penalty, and rebuffering penalty.

While the bandwidth and latency improvement of both WiFi and cellular data networks in the past decades are plenty evident, the extent of signal strength fluctuation and network disruptions (unexpected switching or disconnections) experienced by mobile users in today's network deployment remains less clear. This paper makes three contributions. First, we conduct the first extensive measurement of network disruptions and significant signal strength fluctuations (together denoted as network instabilities) experienced by 2000 smartphones in the wild. Our results show that network disruptions and signal strength fluctuations remains prevalent as we moved into the 4G era. Second, we study how well popular mobile apps today handle such network instabilities. Our results show that even some of the most popular mobile apps do not implement any disruption-tolerant mechanisms. Third, we present Janus, an intelligent interface management framework that exploits the multiple interfaces on a handset to transparently handle network disruptions and satisfy apps' performance requirement. We have implemented a prototype of Janus and our evaluation using a set of popular apps shows that Janus can: 1) transparently and efficiently handle network disruptions; 2) reduce video stalls by 2.9 times and increase 31% of the time of good voice quality; 3) reduce traffic size by 26.4% and energy consumption by 16.3% compared to naive solutions.