Since its advent, TCP has been the overwhelming choice at the transport layer. Developers utilize transport entities of TCP provided by the operating system to implement their network applications. Recently, due to the limitations of TCP and challenges in extending it, noteworthy protocols, such as TLS and QUIC, are emerging. Applications are motivated to try these modern transport protocols for security, performance, flexibility, and extensibility purposes. Unlike TCP, which adheres to the POSIX standards, most entities of these new protocols provide complex project-specific interfaces, making integrating them difficult and may introduce security risks.In this paper, we propose TransPortal, a framework enabling applications to utilize modern transport services flexibly and safely. TransPortal decouples the implementation of applications from underlying entities, and executes transport entities in separate transport agents, which can be replaced without application modification and even runtime. In addition, TransPortal monitors the execution of agents and provides error handling, protecting applications from instability or misuse of new transport entities. We implement a prototype of TransPortal and explore it with several popular transport entities. Extensive evaluation demonstrates that TransPortal provides applications convenience and safety while incurring acceptable overhead.

Interactive applications have deadline requirements, e.g. video conferencing and online gaming. Compared with a single path, which may be less stable or bandwidth insufficient, using multiple network paths simultaneously (e.g., WiFi and cellular network) can leverage the ability of multiple paths to service for the deadline. However, existing multipath schedulers usually ignore the deadline and the influence from subsequent blocks to the current scheduling decision when multiple blocks exist at the sender. In this paper, we propose DAMS, a Deadline-Aware Multipath Scheduler aiming to deliver more blocks with heterogeneous attributes before their deadlines. DAMS carefully schedules the sending order of blocks and balances its allocation on multiple paths to reduce the waste of bandwidth resources with the consideration of the block’s deadline. We implement DAMS with the inspiration of MPQUIC in user space. The extensive experimental results show that DAMS brings 41%-63% performance improvement on average compared with existing multipath solutions.

Recent years have witnessed tremendous growth of video streaming applications. To describe users’ expectations of videos, QoE was proposed, which is critical for content providers. Current video delivery systems optimize QoE with ABR algorithms. However, ABR is usually designed for an abstract "average user" without considering that QoE varies with users. In this paper, to investigate the difference in user preferences, we conduct a user study with 90 subjects and find that the average user can not represent all users. This observation inspires us to propose Ruyi, a video streaming system that incorporates preference awareness into the QoE model and the ABR algorithm. Ruyi profiles QoE preference of users and introduces preference-aware weights over different quality metrics into the QoE model. Based on this QoE model, Ruyi’s ABR is designed to directly predict the influence on metrics after taking different actions. With these predicted metrics, Ruyi chooses the bitrate that maximizes user-specific QoE once the preference is given. Consequently, Ruyi is scalable to different user preferences without re-training the learned models for each user. Simulation results show that Ruyi increases QoE for all users with up to 65.22% improvement. Testbed experimental results show that Ruyi has the highest ratings from subjects.

Applications that allow sharing of user-created short videos exploded in popularity in recent years. A typical short video application allows a user to swipe away the current video being watched and start watching the next video in a video queue. Such user interface causes significant bandwidth waste if users frequently swipe a video away before finishing watching. Solutions to reduce bandwidth waste without impairing the Quality of Experience (QoE) are needed. Solving the problem requires adaptively prefetching of short video chunks, which is challenging as the download strategy needs to match unknown user viewing behavior and network conditions. In our work, we first formulate the problem of adaptive multi-video prefetching in short video streaming. Then, to facilitate the integration and comparison of researchers' algorithms towards solving the problem, we design and implement a discrete-event simulator, which we release as open source. Finally, based on the organization of the Short Video Streaming Grand Challenge at ACM Multimedia 2022, we analyze and summarize the algorithms of the contestants, with the hope of promoting the research community towards addressing this problem.