Short-form video streaming is characterized by fast-paced, scrolling-driven user interactions. This poses unique challenges for its streaming algorithm design. To our knowledge, there is little understanding of how short video streaming algorithms perform in large-scale production platforms. To bridge this gap, this paper reports our two-year experience in evolving the prefetching algorithm, a critical algorithmic component for short video streaming, deployed in a leading global short video service. We adopt an iterative, production-driven approach, progressively evolving the design from simple heuristics to optimization-based and data-driven algorithms, with each iteration validated through large-scale A/B tests on hundreds of millions of users. Our evolution advances two core components: the prefetching logic and the viewing time estimation, including a lightweight on-device personalization mechanism. Through carefully balancing startup delay, mid-playback stalls, bandwidth usage, runtime overhead, and estimation accuracy, we achieve a 0.38% increase in user stay time - our key engagement metric - while simultaneously reducing bandwidth consumption by 14.7% throughout the evolution. We distill actionable insights from real-world deployment, highlighting the importance of startup latency, bandwidth efficiency, and low-overhead design for short video streaming at scale.

We operate one of the largest short video streaming platforms in the world, serving billions of global users every day. Similar to other platforms, we employ Adaptive Bitrate (ABR) streaming, which adaptively selects an appropriate representation from a group of video representations forming a bitrate ladder. In this paper, instead of developing yet another ABR algorithm, we optimize the bitrate ladder configuration, which constitutes the decision space of ABR, by developing a system called TLadder.

In some applications, video content needs to be encoded and uploaded to a remote destination within a pre-defined amount of time. In order to guarantee that the overall processing time does not exceed certain time constraints, a system that performs joint encoding and uploading time control is needed. Such a system requires the flexibility to control both the time spent in the encoding process and the bit-rate. This is because the latter significantly influences the transmission time, especially for transmissions under low-bandwidth constraints. This paper proposes a novel approach to address this challenge by adapting the quantization parameters (QP) of a video encoder in order to meet overall processing time requirements, including both encoding and uploading time. The proposed QP adaptation approach relies on mechanisms to accurately predict the encoding time and bit-rate during the encoding process for the incoming group of pictures. This in turn allows adequate QP selections that result in accurately meeting the overall time constraints. A comprehensive experimental evaluation shows that the proposed QP adaptation approach can accurately meet the overall time constraints by efficiently adapting the encoding process to different target times and bandwidth conditions.

Many applications require video content to be encoded and uploaded under specific complexity constraints. While many speed-ups are available in practical video encoder implementations, it is difficult to predict the impact of such techniques on the actual content being encoded and therefore select the best configuration to meet the given constraints. A method is proposed in this paper to automatically select the encoder configuration in order to meet complexity constraints in terms of encoding and uploading time, using a pre-trained encoder configuration transfer matrix. The algorithm ensures that the content is processed within the specified targets, as presented in the experimental evaluation, where it is shown that the encoder can accurately meet specific constraints under a variety of conditions.

In advanced Peer-to-Peer delivery systems, each user downloads a video stream and at the same time uploads the same stream to other users. In live streaming, scalable video streaming is also proposed to allow partial decoding of the video streaming at a reduced resolution, frame-rate or quality, adapting to different display requirements and receptions conditions of heterogeneous receivers. In live streaming applications, Network Coding (NC) has the potential to bring substantially higher throughput while reducing transmission delays. However, the bandwidth efficiency of NC transmission is still a problem to live media streaming due to the lack of synchronization among peers. To solve this problem, we introduce a scalable streaming system, which includes a layer selection algorithm and a distributed packet scheduling algorithm. The proposed system is tested by streaming a scalable video over a peer-to-peer network. The experimental results confirm that the proposed algorithm achieves better video quality, better delivery ratio, and lower transmission redundancy.

Over the last decade, the emergence of new multimedia devices has motivated the research on efficient media streaming mechanisms that can adapt to dynamic network conditions and heterogeneous devices' capabilities. Network coding (NC) as a rateless code has been applied to collaborative media streaming applications and brings substantial improvements regarding throughput and delay in collaborative media streaming applications. However, little attention has been given to the recoverability of encoded data, especially for the streaming with a strict deadline. When a receiver obtains insufficient packets, it is impossible for receivers to recover any original packets. This in turn leads to severe quality of experience. In this paper, we solve the unrecoverable transmission by determining a scalable layer subscription and scheduling strategy. This scalable layer subscription algorithm is treated as a video quality maximization problem in multiple generations. Then this problem is solved using a dynamic programming algorithm. Experimental results confirm that the proposed algorithm brings better data recoverability and a better quality of service in terms of better video quality, delivery ratio, lower redundancy rate, and significantly lower superfluous video packet rate under different network sizes than conventional random-push schemes.

Network coding (NC) brings substantial improvements in terms of throughput and delay in collaborative media streaming applications. A key aspect of NC-driven live peer-to-peer streaming is the packet scheduling policy. Indeed, lack of synchronization among peers usually results in significantly redundant packet transmission, which in turn leads to severe bandwidth inefficiencies. In this paper, we address the problem of finding a suitable asynchronous packet scheduling policy that greatly helps to overcome this critical redundant transmission problem. We propose a bandwidth cost minimization technique under a full video packet recovery constraint. In order to add scalability and improved performance, we also further derive a distributed packet scheduling algorithm. Both implementation and analytical considerations of the proposed approaches are described in this paper. Experimental results confirm that the proposed algorithms deliver higher bandwidth efficiency with reduced redundancy and communication overhead rate and, consequently, better quality-of-service in terms of improved video quality and delivery ratio.

This paper proposed a distributed packet scheduling algorithm for live peer-to-peer streaming system, where network coding is extended to improve the efficiency in bandwidth utilization. A problem of superfluous packet transmission due to the lack of synchronization among peers is identified. This problem often leads to bandwidth inefficiencies. We solve the problem of finding a suitable asynchronous packet scheduling policy by posing and solving a bandwidth allocation problem. This proposed scheduling policy can reduce the superfluous packet transmission, thereby achieving a more efficient bandwidth usage and an improved quality of service in live streaming applications. Experimental results confirm that the proposed scheme demonstrates significantly better video quality, delivery ratio under different network size and different loss rate compared with other push-based schemes.

This paper proposes a new push-based peer-to-peer (P2P) communication method to optimally transmit scalable video packets over lossy networks. In our scheme, a rate allocation optimization is performed at the sender node. Different from previous sender/receiver driven schemes, our scheme does not need to update buffer maps or request packets periodically, which reduces the amount of redundant packets and yields to less traffic of video data over the Internet. The proposed optimized rate allocation algorithm calculates in advance the number of needed packets according to the cumulative uplink rate between senders and the receiver, and the loss rate of the link. After calculation, each sender sends the hierarchical network coded packets to receivers based on the estimated bandwidth and resource share allocation. With this method, the waste of bandwidth, and the delay caused by the communications among peers can both be reduced.