Low-latency live streaming (LLS) has emerged as a popular web application, with many platforms adopting real-time protocols such as WebRTC to minimize end-to-end latency. However, we observe a counter-intuitive phenomenon: even when the actual encoded bitrate does not fully utilize the available bandwidth, stalling events remain frequent. This insufficient bandwidth utilization arises from the intrinsic temporal variations of real-time video encoding, which cause conventional packet-level congestion control algorithms to misestimate available bandwidth. When a high-bitrate frame is suddenly produced, sending at the wrong rate can either trigger packet loss or increase queueing delay, resulting in playback stalls. To address these issues, we present Camel, a novel frame-level congestion control algorithm (CCA) tailored for LLS. Our insight is to use frame-level network feedback to capture the true network capacity, immune to the irregular sending pattern caused by encoding. Camel comprises three key modules: the Bandwidth and Delay Estimator and the Congestion Detector, which jointly determine the average sending rate, and the Bursting Length Controller, which governs the emission pattern to prevent packet loss. We evaluate Camel on both large-scale real-world deployments and controlled simulations. In the real-world platform with 250M users and 2B sessions across 150+ countries, Camel achieves up to a 70.8% increase in 1080P resolution ratio, a 14.4% increase in media bitrate, and up to a 14.1% reduction in stalling ratio. In simulations under undershooting, shallow buffers, and network jitter, Camel outperforms existing congestion control algorithms, with up to 19.8% higher bitrate, 93.0% lower stalling ratio, and 23.9% improvement in bandwidth estimation accuracy.

Short video applications such as TikTok and Instagram have attracted tremendous attention recently. However, it is very limited for industry and academia to understand the user's Quality of Experience (QoE) on short video, let alone how to improve the QoE in short video streaming.

Viewport-adaptive streaming approaches are emerging as the most promising way to deliver high-quality 360 videos over mobile networks. However, the viewport prediction is only reliable within a short prediction window, i.e., a short playback buffer, which conicts with maintaining a long buffer to avoid playback rebuffering. To deal with this problem, we present RAM360, a Robust Adaptive Multi-layer 360 video streaming system, to ensure high viewport quality and low stall ratio concurrently. We make three technical contributions. First, we design a QoE-driven robust multi-layer streaming framework, where each chunk is encoded by multiple independent layers with different quality levels. The client dynamically decides which chunk and layer to be downloaded by their QoE contributions. Thus, the base-layer could be prefetched to avoid the risk of stalling while the viewport quality is improved by downloading enhancement layer. Second, we establish a novel QoE model to represent the quality of whole playback session, not that of chunk. It aims to maximize the overall QoE of playback session. Third, we introduce the Lyapunov optimization theory to solve the QoE optimization problem, which is an online algorithm with near-optimality solution. We demonstrate that RAM360 can significantly outperform the existing schemes regarding viewport quality, stall ratio, and QoE through extensive experiments with public datasets.

To improve Quality of Experience (QoE) for short video applications, most commercial companies adopt preloading and adaptive streaming technologies concurrently. Though preloading can reduce rebuffering, it may greatly waste bandwidth if the downloaded video chunks are not played. Also, each short video's downloading competes against others, which makes the existing adaptive streaming technologies fail to optimize the QoE for all videos. In this paper, we propose PDAS, a Probability-Driven Adaptive Streaming framework, to minimize the bandwidth waste while guaranteeing QoE simultaneously. We formulate PDAS into an optimization problem, where a probabilistic model is designed to describe the swiping events. Then, the maximum preload size is controlled by the proposed probability-driven max-buffer model, which reduces the bandwidth waste by proactively sleeping. At last, the optimization problem is solved by jointly deciding the preload order and preload bitrate. Extensive experimental results demonstrate that PDAS achieves almost 22.34% gains on QoE and 22.80% reductions on bandwidth usage against the existing methods. As for online evaluation, PDAS ranks first in the ACM MM 2022 Grand Challenge: Short Video Streaming.

The mobile edge caching has made video service providers deliver live 360-degree videos worldwide. However, these services still suffer from the huge network traffic on the core network due to the spherical nature and the diverse requests generated from large user populations. It is challenging to optimize the Quality of Experience (QoE) and the bandwidth consumption simultaneously under the significant number of users as well as dynamic network and playback status. In this paper, we propose a Multi-Agent deep reinforcement learning based 360-degree video streaming system, named MA360, to tackle this multi-user live 360-degree video streaming problem in the context of the edge cache network. Specifically, MA360 employs the Mean Field Actor-Critic (MFAC) algorithm to make clients collaboratively and distributively request tiles aiming at maximizing the overall QoE while minimizing the total bandwidth consumption. Experiments over real-world datasets show that MA360 can improve the QoE while significantly reducing the bandwidth consumption compared with several state-of-the-art edge-assisted 360-degree video streaming strategies.

Short video applications, like TikTok, have attracted many users across the world. It can feed short videos based on users' preferences and allow users to slide the boring content anywhere and anytime. To reduce the loading time and keep playback smoothness, most of the short video apps will preload the recommended short videos in advance. However, these apps preload short videos in fixed size and fixed order, which can lead to huge playback stall and huge bandwidth waste. To deal with these problems, we present an Adaptive Preloading mechanism for short videos based on Lyapunov Optimization, also called APL, to achieve near-optimal playback experience, i.e., maximizing playback smoothness and minimizing bandwidth waste considering users' sliding behaviors. Specifically, we make three technical contributions: (1) We design a novel short video streaming framework which can dynamically preload the recommended short videos before the current video is downloaded completely. (2) We formulate the preloading problem into a playback experience optimization problem to maximize the playback smoothness and minimize the bandwidth waste. (3) We transform the playback experience optimization problem during the whole viewing process into a single-step greedy algorithm based on the Lyapunov optimization theory to make the online decisions during playback. Through extensive experiments based on the real datasets that generously provided by TikTok, we demonstrate that APL can reduce the stall ratio by 81%/12% and bandwidth waste by 11%/31% compared with no-preloading/fixed-preloading mechanism.

To ensure 360-degree video's continuous playback and reduce the bandwidth waste, predicting user's future fixation is indispensable. However, existing methods concentrate either on user's motion information or content information. None of them consider users watching behaviors' inconsistency which embodies user's attention distribution more explicitly. So in this paper, we exploit Cross-Users Behaviors for viewport prediction in 360-degree video adaptive streaming, namely CUB360, trying to concurrently consider user's personalized information and cross-users behaviors information to predict future viewport. Besides, we use a QoE-driven framework to optimize existing video streaming approaches and propose a general algorithm aiming at solving the NP problem at a low complexity. Extensive experimental results over real datasets demonstrate that compared with traditional adaptive streaming method, our proposal can significantly boost the prediction accuracy by 20.2% absolutely and 48.1 % relatively. Besides, the mean quality can get 30.28% gain while quality variance can be reduced by 29.89%.

Recently, there has been a significant interest towards 360-degree panorama video. However, such videos usually require extremely high bitrate which hinders their widely spread over the Internet. Tile-based viewport adaptive streaming is a promising way to deliver 360-degree video due to its on-request portion downloading. But it is not trivial for it to achieve good Quality of Experience (QoE) because Internet request-reply delay is usually much higher than motion-to-photon latency. In this paper, we leverage a probabilistic approach to pre-fetch tiles countering viewport prediction error, and design a QoE-driven viewport adaptation system, 360ProbDASH. It treats user's head movement as probability events, and constructs a probabilistic model to depict the distribution of viewport prediction error. A QoE-driven optimization framework is proposed to minimize total expected distortion of pre-fetched tiles. Besides, to smooth border effects of mixed-rate tiles, the spatial quality variance is also minimized. With the requirement of short-term viewport prediction under a small buffer, it applies a target-buffer-based rate adaptation algorithm to ensure continuous playback. We implement 360ProbDASH prototype and carry out extensive experiments on a simulation test-bed and real-world Internet with real user's head movement traces. The experimental results demonstrate that 360ProbDASH achieves at almost 39% gains on viewport PSNR, and 46% reduction on spatial quality variance against the existed viewport adaptation methods.

The world is becoming more and more virtual than we ever thought it would be. Many video service providers have rolled out 360-degree videos which provide immersive experience to users. However, huge bandwidth occupation of 360-degree video hinders its wide spreading over the Internet. Besides, only part of the video is displayed on the screen, transmitting whole video results in waste of bandwidth and computational resources. Tile-based adaptive streaming is regarded as a bandwidth-friendly approach which only delivers specific portion of the whole video. It requires the clients to decide which portion and at which bitrates to deliver. However, due to both space and time partition of 360-degree videos in tile-based adaptive streaming, there still exists a challenge on the quality inconsistence on spatial and temporal domains. In this paper, we propose a optimal spatial-temporal smoothness approach under restricted network for tile-based adaptive streaming. The bitrates of tiles are determined optimally, aiming at maximizing the overall quality while minimizing the spatial and temporal quality variation. By conducting extensive experiments over real bandwidth dataset and user's head movement traces, our approach can get a significant improvement. Specifically, the Viewport-PSNR can be raised by 24.1% compared with traditional delivery of whole 360 video; while the spatial and temporal stability can be improved by 40.5% and 24.6% respectively compared with tile-based streaming.