[Paper Review] On the Placement and Delivery Schemes for Decentralized Coded Caching System

Qifa Yan, Xiaohu Tang+1 more|arXiv (Cornell University)|Oct 13, 2017

Caching and Content Delivery13 references3 citations

TL;DR

This paper proposes a novel placement and delivery scheme for decentralized coded caching systems that improves rate-memory tradeoff by optimizing cache allocation based on file popularity and enabling greater cooperation across users. By deriving a lower bound on average delivery rate and designing two new greedy-coded delivery algorithms—set-centered and bit-centered—it achieves significant performance gains, especially when file subpacketization F is small compared to user count K.

ABSTRACT

Network based on distributed caching of content is a new architecture to alleviate the ongoing explosive demands for rate of multi-media traffic. In caching networks, coded caching is a recently proposed technique that achieves significant performance gains compared to uncoded caching schemes. In this paper, we derive a lower bound on the average rate with a memory constraint for a family of caching allocation placement and a family of XOR cooperative delivery. The lower bound inspires us how placement and delivery affect the rate memory tradeoff. Based on the clues, we design a new placement and two new delivery algorithms. On one hand, the new placement scheme can allocate the cache more flexibly compared to grouping scheme. On the other hand, the new delivery can exploit more cooperative opportunities compared to the known schemes. The simulations validate our idea.

Motivation & Objective

• Address the inefficiencies in existing decentralized coded caching schemes, particularly the loss from padded zeros in uniform popularity and exclusion of cross-group cooperation in non-uniform popularity.
• Identify a generic family of placement and delivery schemes that generalize existing approaches like the uniform allocation and grouping schemes.
• Derive a theoretical lower bound on the average delivery rate to guide the design of improved placement and delivery algorithms.
• Develop a new placement algorithm that optimizes cache allocation according to file popularity, enabling more flexible and efficient use of memory resources.
• Design two new delivery algorithms—set-centered and bit-centered greedy coded delivery—that exploit more cooperative opportunities across users, reducing delivery rate.

Proposed method

• Formulate a generic framework for decentralized coded caching that includes a family of cache allocation placement and XOR cooperative delivery schemes as special cases.
• Derive a lower bound on the average delivery rate under memory constraints, using a Lagrangian optimization approach to analyze the tradeoff between rate and memory.
• Propose a new placement algorithm that dynamically allocates cache based on file popularity, solving the issue of uniform allocation leading to wasted memory in non-uniform scenarios.
• Introduce two new delivery algorithms: set-centered greedy coded delivery and bit-centered greedy coded delivery, which maximize cooperation across user groups by prioritizing shared content.
• Use asymptotic analysis (as F → ∞) to derive the average delivery rate, showing convergence to a closed-form expression that matches known results under uniform caching.
• Apply a bisection method to numerically solve for optimal cache allocation parameters by inverting a derived function g_l(x), ensuring feasibility under memory and popularity constraints.

Experimental results

Research questions

• RQ1 How does the rate-memory tradeoff in decentralized coded caching depend on the structure of placement and delivery schemes?
• RQ2 What is the theoretical lower bound on the average delivery rate for a given family of placement and delivery algorithms under memory constraints?
• RQ3 Can cache allocation be optimized based on file popularity to improve performance in non-uniform popularity scenarios?
• RQ4 To what extent can cooperative delivery across different user groups be exploited to reduce the delivery rate beyond existing grouping-based schemes?
• RQ5 How do the proposed algorithms perform in practical regimes where F ≪ 2^K, especially when subpacketization is limited?

Key findings

• The proposed lower bound on the average delivery rate provides theoretical insight into how placement and delivery design influence the rate-memory tradeoff.
• The new placement algorithm achieves better cache utilization by allocating more memory to popular files, reducing wasteful padding and improving performance under non-uniform popularity.
• The set-centered greedy coded delivery algorithm achieves a lower delivery rate by prioritizing cooperation among user groups based on shared file requests.
• The bit-centered greedy coded delivery algorithm further improves performance by exploiting fine-grained cooperation at the bit level, outperforming existing schemes in high-load scenarios.
• Simulations confirm that the proposed algorithms significantly reduce the average delivery rate, especially when F ≪ 2^K, where subpacketization constraints limit performance.
• Theoretical analysis shows that in the asymptotic regime (F → ∞), the average delivery rate converges to a closed-form expression that matches known results under uniform caching, validating the correctness of the framework.