StreamOptix: A Cross-layer Adaptive Video Delivery Scheme

TL;DR

StreamOptix addresses the challenge of cross-layer optimization for video delivery in 5G by proposing a three-stage closed-loop framework that jointly leverages PHY, MAC, and APP information. The design integrates a soft link adaptation mechanism at the PHY with OLLA/soft-ACK, a video-aware resource allocator at MAC, and a model-predictive control-based ABR at APP, using regularly sampled link capacity to form a stable, multi-timescale feedback loop. Empirical results on a 5G PDSCH testbed with the Waterloo video set show substantial improvements in PSNR, SSIM, QoE, and rate utilization over traditional baselines, while also delivering distorted bitstreams for richer quality assessment. The work advances cross-layer video delivery by enabling more accurate throughput prediction, tighter bitrate-to-capacity alignment, and robust link adaptation, with available code for reproducibility and potential extension to multimodal communication scenarios.

Abstract

This paper presents a cross-layer video delivery scheme, StreamOptix, and proposes a joint optimization algorithm for video delivery that leverages the characteristics of the physical (PHY), medium access control (MAC), and application (APP) layers. Most existing methods for optimizing video transmission over different layers were developed individually. Realizing a cross-layer design has always been a significant challenge, mainly due to the complex interactions and mismatches in timescales between layers, as well as the presence of distinct objectives in different layers. To address these complications, we take a divide-and-conquer approach and break down the formulated cross-layer optimization problem for video delivery into three sub-problems. We then propose a three-stage closedloop optimization framework, which consists of 1) an adaptive bitrate (ABR) strategy based on the link capacity information from PHY, 2) a video-aware resource allocation scheme accounting for the APP bitrate constraint, and 3) a link adaptation technique utilizing the soft acknowledgment feedback (soft-ACK). The proposed framework also supports the collections of the distorted bitstreams transmitted across the link. This allows a more reasonable assessment of video quality compared to many existing ABR methods that simply neglect the distortions occurring in the PHY layer. Experiments conducted under various network settings demonstrate the effectiveness and superiority of the new cross-layer optimization strategy. A byproduct of this study is the development of more comprehensive performance metrics on video delivery, which lays down the foundation for extending our system to multimodal communications in the future. Code for reproducing the experimental results is available at https://github.com/Evan-sudo/StreamOptix.

Figures (14)

• Figure 1: Trace-driven ABR environment (upper) overlooks the effect of resource allocation and channel fading over wireless link (bottom).
• Figure 2: Evaluation of existing ABR strategies under simulated 5G wireless links. (a) Inconsistency between the predicted QoE and actually received SSIM. (b) Gap between the selected video bitrate and link capacity.
• Figure 3: A schematic diagram of the cross-layer adaptive video delivery system.
• Figure 4: Uniformly spaced throughput in StreamOptix (upper) v.s. non-uniformly spaced throughput in chunk-level meaurement (bottom).
• Figure 5: Evaluation on static link.

Theorems & Definitions (4)

• Theorem 1
• proof
• Theorem 2
• proof